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Introduction to Tensor Calculus

Taha Sochi∗

May 23, 2016

∗Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT.

Email: t.sochi@ucl.ac.uk.

1

2

Preface

These are general notes on tensor calculus originated from a collection of personal notes

which I prepared some time ago for my own use and reference when I was studying the

subject. I decided to put them in the public domain hoping they may be beneﬁcial to some

students in their eﬀort to learn this subject. Most of these notes were prepared in the

form of bullet points like tutorials and presentations and hence some of them may be more

concise than they should be. Moreover, some notes may not be suﬃciently thorough or

general. However this should be understandable considering the level and original purpose

of these notes and the desire for conciseness. There may also be some minor repetition

in some places for the purpose of gathering similar items together, or emphasizing key

points, or having self-contained sections and units.

These notes, in my view, can be used as a short reference for an introductory course on

tensor algebra and calculus. I assume a basic knowledge of calculus and linear algebra

with some commonly used mathematical terminology. I tried to be as clear as possible and

to highlight the key issues of the subject at an introductory level in a concise form. I hope

I have achieved some success in reaching these objectives at least for some of my target

audience. The present text is supposed to be the ﬁrst part of a series of documents about

tensor calculus for gradually increasing levels or tiers. I hope I will be able to ﬁnalize and

publicize the document for the next level in the near future.

CONTENTS 3

Contents

Preface 2

Contents 3

1 Notation, Nomenclature and Conventions 5

2 Preliminaries 10

2.1 Introduction................................... 10

2.2 GeneralRules ................................. 12

2.3 Examples of Tensors of Diﬀerent Ranks . . . . . . . . . . . . . . . . . . . . 15

2.4 ApplicationsofTensors............................. 16

2.5 TypesofTensors ................................ 17

2.5.1 Covariant and Contravariant Tensors . . . . . . . . . . . . . . . . . 17

2.5.2 True and Pseudo Tensors . . . . . . . . . . . . . . . . . . . . . . . . 22

2.5.3 Absolute and Relative Tensors . . . . . . . . . . . . . . . . . . . . . 24

2.5.4 Isotropic and Anisotropic Tensors . . . . . . . . . . . . . . . . . . . 25

2.5.5 Symmetric and Anti-symmetric Tensors . . . . . . . . . . . . . . . . 25

2.6 TensorOperations ............................... 28

2.6.1 Addition and Subtraction . . . . . . . . . . . . . . . . . . . . . . . 28

2.6.2 Multiplication by Scalar . . . . . . . . . . . . . . . . . . . . . . . . 29

2.6.3 Tensor Multiplication . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.6.4 Contraction ............................... 31

2.6.5 InnerProduct.............................. 32

2.6.6 Permutation............................... 34

2.7 Tensor Test: Quotient Rule . . . . . . . . . . . . . . . . . . . . . . . . . . 34

CONTENTS 4

3δand Tensors 36

3.1 Kronecker δ................................... 36

3.2 Permutation .................................. 37

3.3 Useful Identities Involving δor/and ..................... 38

3.3.1 Identities Involving δ.......................... 38

3.3.2 Identities Involving .......................... 40

3.3.3 Identities Involving δand ....................... 42

3.4 Generalized Kronecker delta . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4 Applications of Tensor Notation and Techniques 46

4.1 Common Deﬁnitions in Tensor Notation . . . . . . . . . . . . . . . . . . . 46

4.2 Scalar Invariants of Tensors . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4.3 Common Diﬀerential Operations in Tensor Notation . . . . . . . . . . . . . 49

4.3.1 CartesianSystem............................ 50

4.3.2 Other Coordinate Systems . . . . . . . . . . . . . . . . . . . . . . . 53

4.4 Common Identities in Vector and Tensor Notation . . . . . . . . . . . . . . 56

4.5 Integral Theorems in Tensor Notation . . . . . . . . . . . . . . . . . . . . . 62

4.6 Examples of Using Tensor Techniques to Prove Identities . . . . . . . . . . 63

5 Metric Tensor 76

6 Covariant Diﬀerentiation 79

References 83

1 NOTATION, NOMENCLATURE AND CONVENTIONS 5

1 Notation, Nomenclature and Conventions

•In the present notes we largely follow certain conventions and general notations; most of

which are commonly used in the mathematical literature although they may not be univer-

sally adopted. In the following bullet points we outline these conventions and notations.

We also give initial deﬁnitions of the most basic terms and concepts in tensor calculus;

more thorough technical deﬁnitions will follow, if needed, in the forthcoming sections.

•Scalars are algebraic objects which are uniquely identiﬁed by their magnitude (abso-

lute value) and sign (±), while vectors are broadly geometric objects which are uniquely

identiﬁed by their magnitude (length) and direction in a presumed underlying space.

•At this early stage in these notes, we generically deﬁne “tensor” as an organized array

of mathematical objects such as numbers or functions.

•In generic terms, the rank of a tensor signiﬁes the complexity of its structure. Rank-0

tensors are called scalars while rank-1 tensors are called vectors. Rank-2 tensors may be

called dyads although this, in common use, may be restricted to the outer product of two

vectors and hence is a special case of rank-2 tensors assuming it meets the requirements

of a tensor and hence transforms as a tensor. Like rank-2 tensors, rank-3 tensors may

be called triads. Similar labels, which are much less common in use, may be attached to

higher rank tensors; however, none will be used in the present notes. More generic names

for higher rank tensors, such as polyad, are also in use.

•In these notes we may use “tensor” to mean tensors of all ranks including scalars (rank-0)

and vectors (rank-1). We may also use it as opposite to scalar and vector (i.e. tensor of

rank-nwhere n > 1). In almost all cases, the meaning should be obvious from the context.

•Non-indexed lower case light face Latin letters (e.g. fand h) are used for scalars.

•Non-indexed (lower or upper case) bold face Latin letters (e.g. aand A) are used for

vectors. The exception to this is the basis vectors where indexed bold face lower or upper

case symbols are used. However, there should be no confusion or ambiguity about the

1 NOTATION, NOMENCLATURE AND CONVENTIONS 6

meaning of any one of these symbols.

•Non-indexed upper case bold face Latin letters (e.g. Aand B) are used for tensors (i.e.

of rank >1).

•Indexed light face italic symbols (e.g. aiand Bjk

i) are used to denote tensors of rank >0

in their explicit tensor form (index notation). Such symbols may also be used to denote

the components of these tensors. The meaning is usually transparent and can be identiﬁed

from the context if not explicitly declared.

•Tensor indices in this document are lower case Latin letters usually taken from the

middle of the Latin alphabet like (i, j, k). We also use numbered indices like (i1, i2, . . . , ik)

when the number of tensor indices is variable.

•The present notes are largely based on assuming an underlying orthonormal Cartesian

coordinate system. However, parts of which are based on more general coordinate systems;

in these cases this is stated explicitly or made clear by the content and context.

•Mathematical identities and deﬁnitions may be denoted by using the symbol ‘≡’. How-

ever, for simplicity we will use in the present notes the equality sign “=” to mark identities

and mathematical deﬁnitions as well as normal equalities.

•We use 2D, 3D and nD for two-, three- and n-dimensional spaces. We also use Eq./Eqs.

to abbreviate Equation/Equations.

•Vertical bars are used to symbolize determinants while square brackets are used for

matrices.

•All tensors in the present notes are assumed to be real quantities (i.e. have real rather

than complex components).

•Partial derivative symbol with a subscript index (e.g. i) is frequently used to denote the

ith component of the Cartesian gradient operator ∇:

∂i=∇i=∂

∂xi

(1)

1 NOTATION, NOMENCLATURE AND CONVENTIONS 7

•A comma preceding a subscript index (e.g. , i) is also used to denote partial diﬀerentia-

tion with respect to the ith spatial coordinate in Cartesian systems, e.g.

A,i =∂A

∂xi

(2)

•Partial derivative symbol with a spatial subscript, rather than an index, are used to

denote partial diﬀerentiation with respect to that spatial variable. For instance

∂r=∇r=∂

∂r (3)

is used for the partial derivative with respect to the radial coordinate in spherical coordi-

nate systems identiﬁed by (r, θ, φ) spatial variables.

•Partial derivative symbol with repeated double index is used to denote the Laplacian

operator:

∂ii =∂i∂i=∇2= ∆ (4)

The notation is not aﬀected by using repeated double index other than i(e.g. ∂jj or ∂kk ).

The following notations:

∂2

ii ∂2∂i∂i(5)

are also used in the literature of tensor calculus to symbolize the Laplacian operator.

However, these notations will not be used in the present notes.

•We follow the common convention of using a subscript semicolon preceding a subscript

index (e.g. Akl;i) to symbolize covariant diﬀerentiation with respect to the ith coordinate

(see §6). The semicolon notation may also be attached to the normal diﬀerential operators

to indicate covariant diﬀerentiation (e.g. ∇;ior ∂;ito indicate covariant diﬀerentiation with

respect to the index i).

•All transformation equations in these notes are assumed continuous and real, and all

1 NOTATION, NOMENCLATURE AND CONVENTIONS 8

derivatives are continuous in their domain of variables.

•Based on the continuity condition of the diﬀerentiable quantities, the individual diﬀer-

ential operators in the mixed partial derivatives are commutative, that is

∂i∂j=∂j∂i(6)

•A permutation of a set of objects, which are normally numbers like (1,2, . . . , n) or

symbols like (i, j, k), is a particular ordering or arrangement of these objects. An even

permutation is a permutation resulting from an even number of single-step exchanges

(also known as transpositions) of neighboring objects starting from a presumed original

permutation of these objects. Similarly, an odd permutation is a permutation resulting

from an odd number of such exchanges. It has been shown that when a transformation

from one permutation to another can be done in diﬀerent ways, possibly with diﬀerent

numbers of exchanges, the parity of all these possible transformations is the same, i.e. all

even or all odd, and hence there is no ambiguity in characterizing the transformation from

one permutation to another by the parity alone.

•We normally use indexed square brackets (e.g. [A]iand [∇f]i) to denote the ith compo-

nent of vectors, tensors and operators in their symbolic or vector notation.

•In general terms, a transformation from an nD space to another nD space is a corre-

lation that maps a point from the ﬁrst space (original) to a point in the second space

(transformed) where each point in the original and transformed spaces is identiﬁed by n

independent variables or coordinates. To distinguish between the two sets of coordinates

in the two spaces, the coordinates of the points in the transformed space may be notated

with barred symbols, e.g. (¯x1,¯x2,...,¯xn) or (¯x1,¯x2,...,¯xn) where the superscripts and

subscripts are indices, while the coordinates of the points in the original space are notated

with unbarred symbols, e.g. (x1, x2, . . . , xn) or (x1, x2, . . . , xn). Under certain conditions,

1 NOTATION, NOMENCLATURE AND CONVENTIONS 9

such a transformation is unique and hence an inverse transformation from the transformed

to the original space is also deﬁned. Mathematically, each one of the direct and inverse

transformation can be regarded as a mathematical correlation expressed by a set of equa-

tions in which each coordinate in one space is considered as a function of the coordinates

in the other space. Hence the transformations between the two sets of coordinates in

the two spaces can by expressed mathematically by the following two sets of independent

relations:

¯xi= ¯xi(x1, x2, . . . , xn) & xi=xi(¯x1,¯x2,...,¯xn) (7)

where i= 1,2, . . . , n. An alternative to viewing the transformation as a mapping between

two diﬀerent spaces is to view it as being correlating the same point in the same space but

observed from two diﬀerent coordinate frames of reference which are subject to a similar

transformation.

•Coordinate transformations are described as “proper” when they preserve the handed-

ness (right- or left-handed) of the coordinate system and “improper” when they reverse

the handedness. Improper transformations involve an odd number of coordinate axes

inversions through the origin.

•Inversion of axes may be called improper rotation while ordinary rotation is described

as proper rotation.

•Transformations can be active, when they change the state of the observed object (e.g.

translating the object in space), or passive when they are based on keeping the state of the

object and changing the state of the coordinate system from which the object is observed.

Such distinction is based on an implicit assumption of a more general frame of reference

in the background.

•Finally, tensor calculus is riddled with conﬂicting conventions and terminology. In this

text we will try to use what we believe to be the most common, clear or useful of all of

these.

2 PRELIMINARIES 10

2 Preliminaries

2.1 Introduction

•A tensor is an array of mathematical objects (usually numbers or functions) which

transforms according to certain rules under coordinates change. In a d-dimensional space,

a tensor of rank-nhas dncomponents which may be speciﬁed with reference to a given

coordinate system. Accordingly, a scalar, such as temperature, is a rank-0 tensor with

(assuming 3D space) 30= 1 component, a vector, such as force, is a rank-1 tensor with

31= 3 components, and stress is a rank-2 tensor with 32= 9 components.

•The term “tensor” was originally derived from the Latin word “tensus” which means

tension or stress since one of the ﬁrst uses of tensors was related to the mathematical

description of mechanical stress.

•The dncomponents of a tensor are identiﬁed by ndistinct integer indices (e.g. i, j, k)

which are attached, according to the commonly-employed tensor notation, as superscripts

or subscripts or a mix of these to the right side of the symbol utilized to label the tensor

(e.g. Aijk ,Aijk and Ajk

i). Each tensor index takes all the values over a predeﬁned range

of dimensions such as 1 to din the above example of a d-dimensional space. In general,

all tensor indices have the same range, i.e. they are uniformly dimensioned.

•When the range of tensor indices is not stated explicitly, it is usually assumed to have

the values (1,2,3). However, the range must be stated explicitly or implicitly to avoid

ambiguity.

•The characteristic property of tensors is that they satisfy the principle of invariance un-

der certain coordinate transformations. Therefore, formulating the fundamental physical

laws in a tensor form ensures that they are form-invariant; hence they are objectively-

representing the physical reality and do not depend on the observer. Having the same

form in diﬀerent coordinate systems may be labeled as being “covariant” but this word is

2.1 Introduction 11

also used for a diﬀerent meaning in tensor calculus as explained in §2.5.1.

•“Tensor term” is a product of tensors including scalars and vectors.

•“Tensor expression” is an algebraic sum (or more generally a linear combination) of

tensor terms which may be a trivial sum in the case of a single term.

•“Tensor equality” (symbolized by ‘=’) is an equality of two tensor terms and/or expres-

sions. A special case of this is tensor identity which is an equality of general validity (the

symbol ‘≡’ may be used for identity as well as for deﬁnition).

•The order of a tensor is identiﬁed by the number of its indices (e.g. Ai

jk is a tensor of

order 3) which normally identiﬁes the tensor rank as well. However, when contraction (see

§2.6.4) takes place once or more, the order of the tensor is not aﬀected but its rank is

reduced by two for each contraction operation.1

•“Zero tensor” is a tensor whose all components are zero.

•“Unit tensor” or “unity tensor”, which is usually deﬁned for rank-2 tensors, is a tensor

whose all elements are zero except the ones with identical values of all indices which are

assigned the value 1.

•While tensors of rank-0 are generally represented in a common form of light face non-

indexed symbols, tensors of rank ≥1 are represented in several forms and notations,

the main ones are the index-free notation, which may also be called direct or symbolic or

Gibbs notation, and the indicial notation which is also called index or component or tensor

notation. The ﬁrst is a geometrically oriented notation with no reference to a particular

reference frame and hence it is intrinsically invariant to the choice of coordinate systems,

whereas the second takes an algebraic form based on components identiﬁed by indices

and hence the notation is suggestive of an underlying coordinate system, although being

a tensor makes it form-invariant under certain coordinate transformations and therefore

1In the literature of tensor calculus, rank and order of tensors are generally used interchangeably;

however some authors diﬀerentiate between the two as they assign order to the total number of indices,

including repetitive indices, while they keep rank to the number of free indices. We think the latter is

better and hence we follow this convention in the present text.

2.2 General Rules 12

it possesses certain invariant properties. The index-free notation is usually identiﬁed by

using bold face symbols, like aand B, while the indicial notation is identiﬁed by using

light face indexed symbols such as aiand Bij.

2.2 General Rules

•An index that occurs once in a tensor term is a “free index”.

•An index that occurs twice in a tensor term is a “dummy” or “bound” index.

•No index is allowed to occur more than twice in a legitimate tensor term.2

•A free index should be understood to vary over its range (e.g. 1, . . . , n) and hence it

can be interpreted as saying “for all components represented by the index”. Therefore a

free index represents a number of terms or expressions or equalities equal to the number

of allowed values of its range. For example, when iand jcan vary over the range 1, . . . , n

the following expression

Ai+Bi(8)

represents nseparate expressions while the following equation

Aj

i=Bj

i(9)

represents n×nseparate equations.

•According to the “summation convention”, which is widely used in the literature of

tensor calculus including in the present notes, dummy indices imply summation over their

2We adopt this assertion, which is common in the literature of tensor calculus, as we think it is suitable

for this level. However, there are many instances in the literature of tensor calculus where indices are

repeated more than twice in a single term. The bottom line is that as long as the tensor expression makes

sense and the intention is clear, such repetitions should be allowed with no need in our view to take special

precaution like using parentheses. In particular, the summation convention will not apply automatically

in such cases although summation on such indices can be carried out explicitly, by using the summation

symbol P, or by special declaration of such intention similar to the summation convention. Anyway, in

the present text we will not use indices repeated more than twice in a single term.

2.2 General Rules 13

range, e.g. for an nD space

AiBi≡

n

X

i=1

AiBi=A1B1+A2B2+. . . +AnBn(10)

δij Aij ≡

n

X

i=1

n

X

j=1

δij Aij (11)

ijk Aij Bk≡

n

X

i=1

n

X

j=1

n

X

k=1

ijk Aij Bk(12)

•When dummy indices do not imply summation, the situation must be clariﬁed by en-

closing such indices in parentheses or by underscoring or by using upper case letters (with

declaration of these conventions) or by adding a clarifying comment like “no summation

on repeated indices”.

•Tensors with subscript indices, like Aij , are called covariant, while tensors with super-

script indices, like Ak, are called contravariant. Tensors with both types of indices, like

Almn

lk , are called mixed type. More details about this will follow in §2.5.1.

•Subscript indices, rather than subscripted tensors, are also dubbed “covariant” and

superscript indices are dubbed “contravariant”.

•Each tensor index should conform to one of the variance transformation rules as given

by Eqs. 20 and 21, i.e. it is either covariant or contravariant.

•For orthonormal Cartesian coordinate systems, the two variance types (i.e. covariant

and contravariant) do not diﬀer because the metric tensor is given by the Kronecker delta

(refer to §5and 3.1) and hence any index can be upper or lower although it is common

to use lower indices in such cases.

•For tensor invariance, a pair of dummy indices should in general be complementary

in their variance type, i.e. one covariant and the other contravariant. However, for or-

2.2 General Rules 14

thonormal Cartesian systems the two are the same and hence when both dummy indices

are covariant or both are contravariant it should be understood as an indication that the

underlying coordinate system is orthonormal Cartesian if the possibility of an error is

excluded.

•As indicated earlier, tensor order is equal to the number of its indices while tensor rank is

equal to the number of its free indices; hence vectors (terms, expressions and equalities) are

represented by a single free index and rank-2 tensors are represented by two free indices.

The dimension of a tensor is determined by the range taken by its indices.

•The rank of all terms in legitimate tensor expressions and equalities must be the same.

•Each term in valid tensor expressions and equalities must have the same set of free

indices (e.g. i, j, k).

•A free index should keep its variance type in every term in valid tensor expressions and

equations, i.e. it must be covariant in all terms or contravariant in all terms.

•While free indices should be named uniformly in all terms of tensor expressions and

equalities, dummy indices can be named in each term independently, e.g.

Ai

ik +Bj

jk +Clm

lmk (13)

•A free index in an expression or equality can be renamed uniformly using a diﬀerent

symbol, as long as this symbol is not already in use, assuming that both symbols vary

over the same range, i.e. have the same dimension.

•Examples of legitimate tensor terms, expressions and equalities:

Aij

ij , Aim

m+Bink

nk , Cij =Aij −Bij , a =Bj

j(14)

2.3 Examples of Tensors of Diﬀerent Ranks 15

•Examples of illegitimate tensor terms, expressions and equalities:

Bii

i, Ai+Bij , Ai+Bj, Ai−Bi, Ai

i=Bi,(15)

•Indexing is generally distributive over the terms of tensor expressions and equalities, e.g.

[A+B]i= [A]i+ [B]i(16)

and

[A=B]i⇐⇒ [A]i= [B]i(17)

•Unlike scalars and tensor components, which are essentially scalars in a generic sense,

operators cannot in general be freely reordered in tensor terms, therefore

fh =hf &AiBi=BiAi(18)

but

∂iAi6=Ai∂i(19)

•Almost all the identities in the present notes which are given in a covariant or a con-

travariant or a mixed form are similarly valid for the other forms unless it is stated other-

wise. The objective of reporting in only one form is conciseness and to avoid unnecessary

repetition.

2.3 Examples of Tensors of Diﬀerent Ranks

•Examples of rank-0 tensors (scalars) are energy, mass, temperature, volume and density.

These are totally identiﬁed by a single number regardless of any coordinate system and

hence they are invariant under coordinate transformations.

2.4 Applications of Tensors 16

•Examples of rank-1 tensors (vectors) are displacement, force, electric ﬁeld, velocity and

acceleration. These need for their complete identiﬁcation a number, representing their

magnitude, and a direction representing their geometric orientation within their space.

Alternatively, they can be uniquely identiﬁed by a set of numbers, equal to the number

of dimensions of the underlying space, in reference to a particular coordinate system and

hence this identiﬁcation is system-dependent although they still have system-invariant

properties such as length.

•Examples of rank-2 tensors are Kronecker delta (see §3.1), stress, strain, rate of strain

and inertia tensors. These require for their full identiﬁcation a set of numbers each of

which is associated with two directions.

•Examples of rank-3 tensors are the Levi-Civita tensor (see §3.2) and the tensor of

piezoelectric moduli.

•Examples of rank-4 tensors are the elasticity or stiﬀness tensor, the compliance tensor

and the fourth-order moment of inertia tensor.

•Tensors of high ranks are relatively rare in science and engineering.

•Although rank-0 and rank-1 tensors are, respectively, scalars and vectors, not all scalars

and vectors (in their generic sense) are tensors of these ranks. Similarly, rank-2 tensors

are normally represented by matrices but not all matrices represent tensors.

2.4 Applications of Tensors

•Tensor calculus is very powerful mathematical tool. Tensor notation and techniques

are used in many branches of science and engineering such as ﬂuid mechanics, contin-

uum mechanics, general relativity and structural engineering. Tensor calculus is used for

elegant and compact formulation and presentation of equations and identities in mathe-

matics, science and engineering. It is also used for algebraic manipulation of mathematical

expressions and proving identities in a neat and succinct way (refer to §4.6).

2.5 Types of Tensors 17

•As indicated earlier, the invariance of tensor forms serves a theoretically and practically

important role by allowing the formulation of physical laws in coordinate-free forms.

2.5 Types of Tensors

•In the following subsections we introduce a number of tensor types and categories and

highlight their main characteristics and diﬀerences. These types and categories are not

mutually exclusive and hence they overlap in general; moreover they may not be exhaustive

in their classes as some tensors may not instantiate any one of a complementary set of

types such as being symmetric or anti-symmetric.

2.5.1 Covariant and Contravariant Tensors

•These are the main types of tensor with regard to the rules of their transformation

between diﬀerent coordinate systems.

•Covariant tensors are notated with subscript indices (e.g. Ai) while contravariant tensors

are notated with superscript indices (e.g. Aij ).

•A covariant tensor is transformed according to the following rule

¯

Ai=∂xj

∂¯xiAj(20)

while a contravariant tensor is transformed according to the following rule

¯

Ai=∂¯xi

∂xjAj(21)

where the barred and unbarred symbols represent the same mathematical object (tensor

or coordinate) in the transformed and original coordinate systems respectively.

•An example of covariant tensors is the gradient of a scalar ﬁeld.

•An example of contravariant tensors is the displacement vector.

2.5.1 Covariant and Contravariant Tensors 18

•Some tensors have mixed variance type, i.e. they are covariant in some indices and

contravariant in others. In this case the covariant variables are indexed with subscripts

while the contravariant variables are indexed with superscripts, e.g. Aj

iwhich is covariant

in iand contravariant in j.

•A mixed type tensor transforms covariantly in its covariant indices and contravariantly

in its contravariant indices, e.g.

¯

Al n

m=∂¯xl

∂xi

∂xj

∂¯xm

∂¯xn

∂xkAi k

j(22)

•To clarify the pattern of mathematical transformation of tensors, we explain step-by-

step the practical rules to follow in writing tensor transformation equations between two

coordinate systems, unbarred and barred, where for clarity we color the symbols of the

tensor and the coordinates belonging to the unbarred system with blue while we use

red to mark the symbols belonging to the barred system. Since there are three types

of tensors: covariant, contravariant and mixed, we use three equations in each step. In

this demonstration we use rank-4 tensors as examples since this is suﬃciently general

and hence adequate to elucidate the rules for tensors of any rank. The demonstration

is based on the assumption that the transformation is taking place from the unbarred

system to the barred system; the same rules should apply for the opposite transformation

from the barred system to the unbarred system. We use the sign ‘$’ for the equality in

the transitional steps to indicate that the equalities are under construction and are not

complete.

We start by the very generic equations between the barred tensor ¯

Aand the unbarred

2.5.1 Covariant and Contravariant Tensors 19

tensor Afor the three types:

¯

A$A(covariant)

¯

A$A(contravariant) (23)

¯

A$A(mixed)

We assume that the barred tensor and its coordinates are indexed with ijkl and the

unbarred are indexed with npqr, so we add these indices in their presumed order and

position (lower or upper) paying particular attention to the order in the mixed type:

¯

Aijkl $Anpq r

¯

Aijkl $Anpq r (24)

¯

Aij

kl $Anp

qr

Since the barred and unbarred tensors are of the same type, as they represent the same

tensor in two coordinate systems,3the indices on the two sides of the equalities should

match in their position and order. We then insert a number of partial diﬀerential operators

on the right hand side of the equations equal to the rank of these tensors, which is 4 in our

example. These operators represent the transformation rules for each pair of corresponding

coordinates one from the barred and one from the unbarred:

¯

Aijkl $∂

∂

∂

∂

∂

∂

∂

∂Anpqr

¯

Aijkl $∂

∂

∂

∂

∂

∂

∂

∂Anpqr (25)

¯

Aij

kl $∂

∂

∂

∂

∂

∂

∂

∂Anp

qr

Now we insert the coordinates of the barred system into the partial diﬀerential operators

3Similar basis vectors are assumed.

2.5.1 Covariant and Contravariant Tensors 20

noting that (i) the positions of any index on the two sides should match, i.e. both upper

or both lower, since they are free indices in diﬀerent terms of tensor equalities, (ii) a

superscript index in the denominator of a partial derivative is in lieu of a covariant index

in the numerator4, and (iii) the order of the coordinates should match the order of the

indices in the tensor:

¯

Aijkl $∂

∂xi

∂

∂xj

∂

∂xk

∂

∂xlAnpqr

¯

Aijkl $∂xi

∂

∂xj

∂

∂xk

∂

∂xl

∂Anpqr (26)

¯

Aij

kl $∂xi

∂

∂xj

∂

∂

∂xk

∂

∂xlAnp

qr

For consistency, these coordinates should be barred as they belong to the barred tensor;

hence we add bars:

¯

Aijkl $∂

∂¯xi

∂

∂¯xj

∂

∂¯xk

∂

∂¯xlAnpqr

¯

Aijkl $∂¯xi

∂

∂¯xj

∂

∂¯xk

∂

∂¯xl

∂Anpqr (27)

¯

Aij

kl $∂¯xi

∂

∂¯xj

∂

∂

∂¯xk

∂

∂¯xlAnp

qr

Finally, we insert the coordinates of the unbarred system into the partial diﬀerential

operators noting that (i) the positions of the repeated indices on the same side should

be opposite, i.e. one upper and one lower, since they are dummy indices and hence the

position of the index of the unbarred coordinate should be opposite to its position in the

unbarred tensor, (ii) an upper index in the denominator is in lieu of a lower index in the

numerator, and (iii) the order of the coordinates should match the order of the indices in

4The use of upper indices in the denominator of partial derivatives, which is common in this type of

equations, is to indicate the fact that the coordinates and their diﬀerentials transform contravariantly.

2.5.1 Covariant and Contravariant Tensors 21

the tensor:

¯

Aijkl =∂xn

∂¯xi

∂xp

∂¯xj

∂xq

∂¯xk

∂xr

∂¯xlAnpqr

¯

Aijkl =∂¯xi

∂xn

∂¯xj

∂xp

∂¯xk

∂xq

∂¯xl

∂xrAnpqr (28)

¯

Aij

kl =∂¯xi

∂xn

∂¯xj

∂xp

∂xq

∂¯xk

∂xr

∂¯xlAnp

qr

We also replaced the ‘$’ sign in the ﬁnal set of equations with the strict equality sign ‘=’

as the equations now are complete.

•A tensor of mcontravariant indices and ncovariant indices may be called type (m, n)

tensor, e.g. Ak

ij is a type (1,2) tensor. When one or both variance types are absent, zero

is used to refer to the absent type in this notation, e.g. Bik is a type (2,0) tensor.

•The covariant and contravariant types of a tensor are linked through the metric tensor

(refer to §5).

•For orthonormal Cartesian systems there is no diﬀerence between covariant and con-

travariant tensors, and hence the indices can be upper or lower.

•The vectors providing the basis set (not necessarily of unit length or mutually orthogonal)

for a coordinate system are of covariant type when they are tangent to the coordinate axes,

and they are of contravariant type when they are perpendicular to the local surfaces of

constant coordinates. These two sets are identical for orthonormal Cartesian systems.

•Formally, the covariant and contravariant basis vectors are given respectively by:

Ei=∂r

∂ui&Ei=∇ui(29)

where ris the position vector in Cartesian coordinates and uiis a generalized curvilinear

coordinate. As indicated already, a superscript in the denominator of partial derivatives

is equivalent to a subscript in the numerator.

•In general, the covariant and contravariant basis vectors are not mutually orthogonal

2.5.2 True and Pseudo Tensors 22

or of unit length; however the two sets are reciprocal systems and hence they satisfy the

following reciprocity relation:

Ei·Ej=δj

i(30)

where δj

iis the Kronecker delta (refer to §3.1).

•A vector can be represented either by covariant components with contravariant coordi-

nate basis vectors or by contravariant components with covariant coordinate basis vectors.

For example, a vector Acan be expressed as

A=AiEior A=AiEi(31)

where Eiand Eiare the contravariant and covariant basis vectors respectively. The use of

the covariant or contravariant form of the vector representation is a matter of choice and

convenience.

•More generally, a tensor of any rank (≥1) can be represented covariantly using con-

travariant basis tensors of that rank, or contravariantly using covariant basis tensors, or

in a mixed form using a mixed basis of opposite type. For example, a rank-2 tensor Acan

be written as:

A=Aij EiEj=Aij EiEj=Aj

iEiEj(32)

where EiEj,EiEjand EiEjare dyadic products (refer to §2.6.3).

2.5.2 True and Pseudo Tensors

•These are also called polar and axial tensors respectively although it is more common

to use the latter terms for vectors. Pseudo tensors may also be called tensor densities.

•True tensors are proper (or ordinary) tensors and hence they are invariant under co-

ordinate transformations, while pseudo tensors are not proper tensors since they do not

transform invariantly as they acquire a minus sign under improper orthogonal transfor-

2.5.2 True and Pseudo Tensors 23

mations which involve inversion of coordinate axes through the origin with a change of

system handedness.

•Because true and pseudo tensors have diﬀerent mathematical properties and represent

diﬀerent types of physical entities, the terms of consistent tensor expressions and equations

should be uniform in their true and pseudo type, i.e. all terms are true or all are pseudo.

•The direct product (refer to §2.6.3) of even number of pseudo tensors is a proper tensor,

while the direct product of odd number of pseudo tensors is a pseudo tensor. The direct

product of true tensors is obviously a true tensor.

•The direct product of a mix of true and pseudo tensors is a true or pseudo tensor

depending on the number of pseudo tensors involved in the product as being even or odd

respectively.

•Similar rules to those of direct product apply to cross products (including curl operations)

involving tensors (usually of rank-1) with the addition of a pseudo factor for each cross

product operation. This factor is contributed by the permutation tensor (see §3.2) which

is implicit in the deﬁnition of the cross product (see Eqs. 121 and 146).

•In summary, what determines the tensor type (true or pseudo) of the tensor terms in-

volving direct5and cross products is the parity of the multiplicative factors of pseudo type

plus the number of cross product operations involved since each cross product contributes

an factor.

•Examples of true scalars are temperature, mass and the dot product of two polar or two

axial vectors, while examples of pseudo scalars are the dot product of an axial vector and

a polar vector and the scalar triple product of polar vectors.

•Examples of polar vectors are displacement and acceleration, while examples of axial

vectors are angular velocity and cross product of polar vectors in general (including curl

operation on polar vectors) due to the involvement of the permutation symbol which is

5Inner product (see §2.6.5) is the result of a direct product operation followed by a contraction (see

§2.6.4) and hence it is a direct product in this context.

2.5.3 Absolute and Relative Tensors 24

a pseudo tensor (refer to §3.2). The essence of this distinction is that the direction of a

pseudo vector depends on the observer choice of the handedness of the coordinate system

whereas the direction of a proper vector is independent of such choice.

•Examples of proper tensors of rank-2 are stress and rate of strain tensors, while examples

of pseudo tensors of rank-2 are direct products of two vectors: one polar and one axial.

•Examples of proper tensors of higher ranks are piezoelectric moduli tensor (rank-3)

and elasticity tensor (rank-4), while examples of pseudo tensors of higher ranks are the

permutation tensor of these ranks.

2.5.3 Absolute and Relative Tensors

•Considering an arbitrary transformation from a general coordinate system to another, a

relative tensor of weight wis deﬁned by the following tensor transformation:

¯

Aij...k

lm...n =

∂x

∂¯x

w∂¯xi

∂xa

∂¯xj

∂xb· · · ∂¯xk

∂xc

∂xd

∂¯xl

∂xe

∂¯xm· · · ∂xf

∂¯xnAab...c

de...f (33)

where ∂x

∂¯xis the Jacobian of the transformation between the two systems. When w= 0

the tensor is described as an absolute or true tensor, while when w=−1 the tensor is

described as a pseudo tensor. When w= 1 the tensor may be described as a tensor

density.6

•As indicated earlier, a tensor of mcontravariant indices and ncovariant indices may be

called type (m, n). This may be generalized to include the weight as a third entry and

hence the type of the tensor is identiﬁed by (m, n, w).

•Relative tensors can be added and subtracted if they are of the same variance type and

have the same weight; the result is a tensor of the same type and weight. Also, relative

tensors can be equated if they are of the same type and weight.

•Multiplication of relative tensors produces a relative tensor whose weight is the sum of

6Some of these labels are used diﬀerently by diﬀerent authors.

2.5.4 Isotropic and Anisotropic Tensors 25

the weights of the original tensors. Hence, if the weights are added up to a non-zero value

the result is a relative tensor of that weight; otherwise it is an absolute tensor.

2.5.4 Isotropic and Anisotropic Tensors

•Isotropic tensors are characterized by the property that the values of their components

are invariant under coordinate transformation by proper rotation of axes. In contrast, the

values of the components of anisotropic tensors are dependent on the orientation of the

coordinate axes. Notable examples of isotropic tensors are scalars (rank-0), the vector 0

(rank-1), Kronecker delta δij (rank-2) and Levi-Civita tensor ijk (rank-3). Many tensors

describing physical properties of materials, such as stress and magnetic susceptibility, are

anisotropic.

•Direct and inner products of isotropic tensors are isotropic tensors.

•The zero tensor of any rank is isotropic; therefore if the components of a tensor vanish

in a particular coordinate system they will vanish in all properly and improperly rotated

coordinate systems.7Consequently, if the components of two tensors are identical in a

particular coordinate system they are identical in all transformed coordinate systems.

•As indicated, all rank-0 tensors (scalars) are isotropic. Also, the zero vector, 0, of any

dimension is isotropic; in fact it is the only rank-1 isotropic tensor.

2.5.5 Symmetric and Anti-symmetric Tensors

•These types of tensor apply to high ranks only (rank ≥2). Moreover, these types are

not exhaustive, even for tensors of ranks ≥2, as there are high-rank tensors which are

neither symmetric nor anti-symmetric.

7For improper rotation, this is more general than being isotropic.

2.5.5 Symmetric and Anti-symmetric Tensors 26

•A rank-2 tensor Aij is symmetric iﬀ for all iand j

Aji =Aij (34)

and anti-symmetric or skew-symmetric iﬀ

Aji =−Aij (35)

Similar conditions apply to contravariant type tensors (refer also to the following).

•A rank-ntensor Ai1...inis symmetric in its two indices ijand iliﬀ

Ai1...il...ij...in=Ai1...ij...il...in(36)

and anti-symmetric or skew-symmetric in its two indices ijand iliﬀ

Ai1...il...ij...in=−Ai1...ij...il...in(37)

•Any rank-2 tensor Aij can be synthesized from (or decomposed into) a symmetric part

A(ij)(marked with round brackets enclosing the indices) and an anti-symmetric part A[ij]

(marked with square brackets) where

Aij =A(ij)+A[ij], A(ij)=1

2(Aij +Aji ) & A[ij ]=1

2(Aij −Aji ) (38)

•A rank-3 tensor Aijk can be symmetrized by

A(ijk)=1

3! (Aijk +Akij +Aj ki +Aikj +Aj ik +Akj i) (39)

2.5.5 Symmetric and Anti-symmetric Tensors 27

and anti-symmetrized by

A[ijk]=1

3! (Aijk +Akij +Aj ki −Aikj −Aj ik −Akj i) (40)

•A rank-ntensor Ai1...incan be symmetrized by

A(i1...in)=1

n!(sum of all even & odd permutations of indices i’s) (41)

and anti-symmetrized by

A[i1...in]=1

n!(sum of all even permutations minus sum of all odd permutations) (42)

•For a symmetric tensor Aij and an anti-symmetric tensor Bij (or the other way around)

we have

Aij Bij = 0 (43)

•The indices whose exchange deﬁnes the symmetry and anti-symmetry relations should

be of the same variance type, i.e. both upper or both lower.

•The symmetry and anti-symmetry characteristic of a tensor is invariant under coordinate

transformation.

•A tensor of high rank (>2) may be symmetrized or anti-symmetrized with respect to

only some of its indices instead of all of its indices, e.g.

A(ij)k=1

2(Aijk +Ajik) & A[ij]k=1

2(Aijk −Ajik) (44)

•A tensor is totally symmetric iﬀ

Ai1...in=A(i1...in)(45)

2.6 Tensor Operations 28

and totally anti-symmetric iﬀ

Ai1...in=A[i1...in](46)

•For a totally skew-symmetric tensor (i.e. anti-symmetric in all of its indices), nonzero

entries can occur only when all the indices are diﬀerent.

2.6 Tensor Operations

•There are many operations that can be performed on tensors to produce other tensors

in general. Some examples of these operations are addition/subtraction, multiplication

by a scalar (rank-0 tensor), multiplication of tensors (each of rank >0), contraction and

permutation. Some of these operations, such as addition and multiplication, involve more

than one tensor while others are performed on a single tensor, such as contraction and

permutation.

•In tensor algebra, division is allowed only for scalars, hence if the components of an

indexed tensor should appear in a denominator, the tensor should be redeﬁned to avoid

this, e.g. Bi=1

Ai.

2.6.1 Addition and Subtraction

•Tensors of the same rank and type (covariant/contravariant/mixed and true/pseudo)

can be added algebraically to produce a tensor of the same rank and type, e.g.

a=b+c(47)

Ai=Bi−Ci(48)

2.6.2 Multiplication by Scalar 29

Ai

j=Bi

j+Ci

j(49)

•The added/subtracted terms should have the same indicial structure with regard to

their free indices, as explained in §2.2, hence Ai

jk and Bj

ik cannot be added or subtracted

although they are of the same rank and type, but Ami

mjk and Bi

jk can be added and sub-

tracted.

•Addition of tensors is associative and commutative:

(A+B) + C=A+ (B+C) (50)

A+B=B+A(51)

2.6.2 Multiplication by Scalar

•A tensor can be multiplied by a scalar, which generally should not be zero, to produce

a tensor of the same variance type and rank, e.g.

Aj

ik =aBj

ik (52)

where ais a non-zero scalar.

•As indicated above, multiplying a tensor by a scalar means multiplying each component

of the tensor by that scalar.

•Multiplication by a scalar is commutative, and associative when more than two factors

are involved.

2.6.3 Tensor Multiplication 30

2.6.3 Tensor Multiplication

•This may also be called outer or exterior or direct or dyadic multiplication, although

some of these names may be reserved for operations on vectors.

•On multiplying each component of a tensor of rank rby each component of a tensor of

rank k, both of dimension m, a tensor of rank (r+k) with mr+kcomponents is obtained

where the variance type of each index (covariant or contravariant) is preserved, e.g.

AiBj=Cij (53)

Aij Bkl =Cij

kl (54)

•The outer product of a tensor of type (m, n) by a tensor of type (p, q) results in a tensor

of type (m+p, n +q).

•Direct multiplication of tensors may be marked by the symbol , mostly when using

symbolic notation for tensors, e.g. AB. However, in the present text no symbol will be

used for the operation of direct multiplication.

•Direct multiplication of tensors is not commutative.

•The outer product operation is distributive with respect to the algebraic sum of tensors:

A(B±C) = AB ±AC & (B±C)A=BA ±CA (55)

•Multiplication of a tensor by a scalar (refer to §2.6.2) may be regarded as a special case

of direct multiplication.

•The rank-2 tensor constructed as a result of the direct multiplication of two vectors is

commonly called dyad.

•Tensors may be expressed as an outer product of vectors where the rank of the resultant

2.6.4 Contraction 31

product is equal to the number of the vectors involved (e.g. 2 for dyads and 3 for triads).

•Not every tensor can be synthesized as a product of lower rank tensors.

•In the outer product, it is understood that all the indices of the involved tensors have

the same range.

•The outer product of tensors yields a tensor.

2.6.4 Contraction

•Contraction of a tensor of rank >1 is to make two free indices identical, by unifying

their symbols, and perform summation over these repeated indices, e.g.

Aj

icontraction

−−−−−−−−→ Ai

i(56)

Ajk

il contraction on jl

−−−−−−−−−−−−→ Amk

im (57)

•Contraction results in a reduction of the rank by 2 since it implies the annihilation of

two free indices. Therefore, the contraction of a rank-2 tensor is a scalar, the contraction

of a rank-3 tensor is a vector, the contraction of a rank-4 tensor is a rank-2 tensor, and so

on.

•For general non-Cartesian coordinate systems, the pair of contracted indices should be

diﬀerent in their variance type, i.e. one upper and one lower. Hence, contraction of a

mixed tensor of type (m, n) will, in general, produce a tensor of type (m−1, n −1).

•A tensor of type (p, q) can have p×qpossible contractions, i.e. one contraction for each

pair of lower and upper indices.

•A common example of contraction is the dot product operation on vectors which can be

regarded as a direct multiplication (refer to §2.6.3) of the two vectors, which results in a

rank-2 tensor, followed by a contraction.

2.6.5 Inner Product 32

•In matrix algebra, taking the trace (summing the diagonal elements) can also be consid-

ered as contraction of the matrix, which under certain conditions can represent a rank-2

tensor, and hence it yields the trace which is a scalar.

•Applying the index contraction operation on a tensor results into a tensor.

•Application of contraction of indices operation on a relative tensor (see §2.5.3) produces

a relative tensor of the same weight as the original tensor.

2.6.5 Inner Product

•On taking the outer product (refer to §2.6.3) of two tensors of rank ≥1 followed by a

contraction on two indices of the product, an inner product of the two tensors is formed.

Hence if one of the original tensors is of rank-mand the other is of rank-n, the inner

product will be of rank-(m+n−2).

•The inner product operation is usually symbolized by a single dot between the two

tensors, e.g. A·B, to indicate contraction following outer multiplication.

•In general, the inner product is not commutative. When one or both of the tensors

involved in the inner product are of rank >1 the order of the multiplicands does matter.

•The inner product operation is distributive with respect to the algebraic sum of tensors:

A·(B±C) = A·B±A·C& (B±C)·A=B·A±C·A(58)

•As indicated before (see §2.6.4), the dot product of two vectors is an example of the

inner product of tensors, i.e. it is an inner product of two rank-1 tensors to produce a

rank-0 tensor:

[ab]j

i=aibjcontraction

−−−−−−−−→ a·b=aibi(59)

•Another common example (from linear algebra) of inner product is the multiplication of

a matrix (representing a rank-2 tensor assuming certain conditions) by a vector (rank-1

2.6.5 Inner Product 33

tensor) to produce a vector, e.g.

[Ab]k

ij =Aij bkcontraction on jk

−−−−−−−−−−−−−→ [A·b]i=Aij bj(60)

The multiplication of two n×nmatrices is another example of inner product (see Eq.

119).

•For tensors whose outer product produces a tensor of rank >2, various contraction

operations between diﬀerent sets of indices can occur and hence more than one inner

product, which are diﬀerent in general, can be deﬁned. Moreover, when the outer product

produces a tensor of rank >3 more than one contraction can take place simultaneously.

•There are more specialized types of inner product; some of which may be deﬁned dif-

ferently by diﬀerent authors. For example, a double inner product of two rank-2 tensors,

Aand B, may be deﬁned and denoted by double vertically- or horizontally-aligned dots

(e.g. A:Bor A· ·B) to indicate double contraction taking place between diﬀerent pairs

of indices. An instance of these types is the inner product with double contraction of two

dyads which is commonly deﬁned by8

ab:cd = (a·c) (b·d) (61)

and the result is a scalar. The single dots in the right hand side of the last equation

symbolize the conventional dot product of two vectors. Some authors may deﬁne a diﬀerent

type of double-contraction inner product of two dyads, symbolized by two horizontally-

aligned dots, which may be called a “transposed contraction”, and is given by

ab · ·cd =ab:dc = (a·d) (b·c) (62)

8It is also deﬁned diﬀerently by some authors.

2.6.6 Permutation 34

where the result is also a scalar. However, diﬀerent authors may have diﬀerent conventions

and hence one should be vigilant about such diﬀerences.

•For two rank-2 tensors, the aforementioned double-contraction inner products are simi-

larly deﬁned as in the case of two dyads:

A:B=Aij Bij &A· ·B=Aij Bji (63)

•Inner products with higher multiplicity of contractions are similarly deﬁned, and hence

can be regarded as trivial extensions of the inner products with lower contraction multi-

plicities.

•The inner product of tensors produces a tensor because the inner product is an outer

product operation followed by an index contraction operation and both of these operations

on tensors produce tensors.

2.6.6 Permutation

•A tensor may be obtained by exchanging the indices of another tensor, e.g. transposition

of rank-2 tensors.

•Tensor permutation applies only to tensors of rank ≥2.

•The collection of tensors obtained by permuting the indices of a basic tensor may be

called isomers.

2.7 Tensor Test: Quotient Rule

•Sometimes a tensor-like object may be suspected for being a tensor; in such cases a test

based on the “quotient rule” can be used to clarify the situation. According to this rule, if

the inner product of a suspected tensor with a known tensor is a tensor then the suspect

is a tensor. In more formal terms, if it is not known if Ais a tensor but it is known that

2.7 Tensor Test: Quotient Rule 35

Band Care tensors; moreover it is known that the following relation holds true in all

rotated (properly-transformed) coordinate frames:

Apq...k...m Bij...k...n =Cpq...mij...n (64)

then Ais a tensor. Here, A,Band Care respectively of ranks m, n and (m+n−2), due

to the contraction on kwhich can be any index of Aand Bindependently.

•Testing for being a tensor can also be done by applying ﬁrst principles through direct

substitution in the transformation equations. However, using the quotient rule is generally

more convenient and requires less work.

•The quotient rule may be considered as a replacement for the division operation which

is not deﬁned for tensors.

3δAND TENSORS 36

3δand Tensors

•These tensors are of particular importance in tensor calculus due to their distinctive

properties and unique transformation attributes. They are numerical tensors with ﬁxed

components in all coordinate systems. The ﬁrst is called Kronecker delta or unit ten-

sor, while the second is called Levi-Civita9, permutation, anti-symmetric and alternating

tensor.

•The δand tensors are conserved under coordinate transformations and hence they are

the same for all systems of coordinate.10

3.1 Kronecker δ

•This is a rank-2 symmetric tensor in all dimensions, i.e.

δij =δji (i, j = 1,2, . . . , n) (65)

Similar identities apply to the contravariant and mixed types of this tensor.

•It is invariant in all coordinate systems, and hence it is an isotropic tensor.11

•It is deﬁned as:

δij =

1 (i=j)

0 (i6=j)

(66)

9This name is usually used for the rank-3 tensor. Also some authors distinguish between the permuta-

tion tensor and Levi-Civita tensor even for rank-3. Moreover, some of the common labels and descriptions

of are more speciﬁc to rank-3.

10For the permutation tensor, the statement applies to proper coordinate transformations.

11In fact it is more general than isotropic as it is invariant even under improper coordinate transfor-

mations.

3.2 Permutation 37

and hence it can be considered as the identity matrix, e.g. for 3D

[δij ] =

δ11 δ12 δ13

δ21 δ22 δ23

δ31 δ32 δ33

=

1 0 0

0 1 0

0 0 1

(67)

•Covariant, contravariant and mixed type of this tensor are the same, that is

δi

j=δj

i=δij =δij (68)

3.2 Permutation

•This is an isotropic tensor. It has a rank equal to the number of dimensions; hence, a

rank-npermutation tensor has nncomponents.

•It is totally anti-symmetric in each pair of its indices, i.e. it changes sign on swapping

any two of its indices, that is

i1...ik...il...in=−i1...il...ik...in(69)

The reason is that any exchange of two indices requires an even/odd number of single-

step shifts to the right of the ﬁrst index plus an odd/even number of single-step shifts to

the left of the second index, so the total number of shifts is odd and hence it is an odd

permutation of the original arrangement.

•It is a pseudo tensor since it acquires a minus sign under improper orthogonal transfor-

mation of coordinates (inversion of axes with possible superposition of rotation).

•Deﬁnition of rank-2 (ij ):

12 = 1, 21 =−1 & 11 =22 = 0 (70)

3.3 Useful Identities Involving δor/and 38

•Deﬁnition of rank-3 (ijk ):

ijk =

1 (i, j, k is even permutation of 1,2,3)

−1 (i, j, k is odd permutation of 1,2,3)

0 (repeated index)

(71)

•The deﬁnition of rank-n (i1i2...in) is similar to the deﬁnition of rank-3 considering

index repetition and even or odd permutations of its indices (i1, i2,· · · , in) corresponding

to (1,2,· · · , n), that is

i1i2...in=

1 [(i1, i2, . . . , in) is even permutation of (1,2, . . . , n)]

−1 [(i1, i2, . . . , in) is odd permutation of (1,2, . . . , n)]

0 [repeated index]

(72)

•may be considered a contravariant relative tensor of weight +1 or a covariant relative

tensor of weight −1. Hence, in 2D, 3D and nD spaces respectively we have:

ij =ij (73)

ijk =ijk (74)

i1i2...in=i1i2...in(75)

3.3 Useful Identities Involving δor/and

3.3.1 Identities Involving δ

•When an index of the Kronecker delta is involved in a contraction operation by repeating

an index in another tensor in its own term, the eﬀect of this is to replace the shared index

3.3.1 Identities Involving δ39

in the other tensor by the other index of the Kronecker delta, that is

δij Aj=Ai(76)

In such cases the Kronecker delta is described as the substitution or index replacement

operator. Hence,

δij δjk =δik (77)

Similarly,

δij δjk δki =δik δki =δii =n(78)

where nis the space dimension.

•Because the coordinates are independent of each other:

∂xi

∂xj

=∂jxi=xi,j =δij (79)

Hence, in an nD space we have

∂ixi=δii =n(80)

•For orthonormal Cartesian systems:

∂xi

∂xj=∂xj

∂xi=δij =δij (81)

•For a set of orthonormal basis vectors in orthonormal Cartesian systems:

ei·ej=δij (82)

•The double inner product of two dyads formed by orthonormal basis vectors of an

3.3.2 Identities Involving 40

orthonormal Cartesian system is given by:

eiej:ekel=δikδj l (83)

3.3.2 Identities Involving

•For rank-3 :

ijk =kij =j ki =−ikj =−j ik =−kj i (sense of cyclic order) (84)

These equations demonstrate the fact that rank-3 is totally anti-symmetric in all of its

indices since a shift of any two indices reverses the sign. This also reﬂects the fact that

the above tensor system has only one independent component.

•For rank-2 :

ij = (j−i) (85)

•For rank-3 :

ijk =1

2(j−i) (k−i) (k−j) (86)

•For rank-4 :

ijkl =1

12 (j−i) (k−i) (l−i) (k−j) (l−j) (l−k) (87)

•For rank-n :

a1a2···an=

n−1

Y

i=1 "1

i!

n

Y

j=i+1

(aj−ai)#=1

S(n−1) Y

1≤i<j≤n

(aj−ai) (88)

3.3.2 Identities Involving 41

where S(n−1) is the super-factorial function of (n−1) which is deﬁned as

S(k) =

k

Y

i=1

i! = 1! ·2! ·. . . ·k! (89)

A simpler formula for rank-n can be obtained from the previous one by ignoring the

magnitude of the multiplication factors and taking only their signs, that is

a1a2···an=Y

1≤i<j≤n

σ(aj−ai) = σ Y

1≤i<j≤n

(aj−ai)!(90)

where

σ(k) =

+1 (k > 0)

−1 (k < 0)

0 (k= 0)

(91)

•For rank-n :

i1i2···ini1i2···in=n! (92)

because this is the sum of the squares of i1i2···inover all the permutations of ndiﬀerent

indices which is equal to n! where the value of of each one of these permutations is either

+1 or −1 and hence in both cases their square is 1.

•For a symmetric tensor Aj k :

ijk Ajk = 0 (93)

because an exchange of the two indices of Ajk does not aﬀect its value due to the symmetry

whereas a similar exchange in these indices in ijk results in a sign change; hence each term

in the sum has its own negative and therefore the total sum will vanish.

•

ijk AiAj=ijkAiAk=ij k AjAk= 0 (94)

3.3.3 Identities Involving δand 42

because, due to the commutativity of multiplication, an exchange of the indices in A’s will

not aﬀect the value but a similar exchange in the corresponding indices of ijk will cause

a change in sign; hence each term in the sum has its own negative and therefore the total

sum will be zero.

•For a set of orthonormal basis vectors in a 3D space with a right-handed orthonormal

Cartesian coordinate system:

ei×ej=ijk ek(95)

ei·(ej×ek) = ijk (96)

3.3.3 Identities Involving δand

•

ijk δ1iδ2jδ3k=123 = 1 (97)

•For rank-2 :

ij kl =

δik δil

δjk δjl

=δikδj l −δilδjk (98)

ilkl =δik (99)

ij ij = 2 (100)

3.3.3 Identities Involving δand 43

•For rank-3 :

ijk lmn =

δil δim δin

δjl δjm δjn

δkl δkm δkn

=δilδj mδkn+δim δjn δkl +δinδjl δkm−δilδj nδk m−δim δjl δkn −δinδjm δkl

(101)

ijk lmk =

δil δim

δjl δjm

=δilδj m −δimδj l (102)

The last identity is very useful in manipulating and simplifying tensor expressions and

proving vector and tensor identities.

ijk ljk = 2δil (103)

ijk ijk = 2δii = 6 (104)

since the rank and dimension of are the same, which is 3 in this case.

•For rank-n :

i1i2···inj1j2···jn=

δi1j1δi1j2· · · δi1jn

δi2j1δi2j2· · · δi2jn

.

.

..

.

.....

.

.

δinj1δinj2· · · δinjn

(105)

•According to Eqs. 71 and 76:

ijk δij =ijkδik =ij k δjk = 0 (106)

3.4 Generalized Kronecker delta 44

3.4 Generalized Kronecker delta

•The generalized Kronecker delta is deﬁned by:

δi1...in

j1...jn=

1 [(j1. . . jn) is even permutation of (i1. . . in)]

−1 [(j1. . . jn) is odd permutation of (i1. . . in)]

0 [repeated j’s]

(107)

It can also be deﬁned by the following n×ndeterminant:

δi1...in

j1...jn=

δi1

j1δi1

j2· · · δi1

jn

δi2

j1δi2

j2· · · δi2

jn

.

.

..

.

.....

.

.

δin

j1δin

j2· · · δin

jn

(108)

where the δi

jentries in the determinant are the normal Kronecker delta as deﬁned by Eq.

66.

•Accordingly, the relation between the rank-n and the generalized Kronecker delta in

an nD space is given by:

i1i2...in=δ1 2...n

i1i2...in&i1i2...in=δi1i2...in

1 2...n (109)

Hence, the permutation tensor may be considered as a special case of the generalized

Kronecker delta. Consequently the permutation symbol can be written as an n×ndeter-

minant consisting of the normal Kronecker deltas.

•If we deﬁne

δij

lm =δijk

lmk (110)

4 APPLICATIONS OF TENSOR NOTATION AND TECHNIQUES 46

4 Applications of Tensor Notation and Techniques

4.1 Common Deﬁnitions in Tensor Notation

•The trace of a matrix Arepresenting a rank-2 tensor is:

tr (A) = Aii (113)

•For a 3 ×3 matrix representing a rank-2 tensor in a 3D space, the determinant is:

det (A) =

A11 A12 A13

A21 A22 A23

A31 A32 A33

=ijk A1iA2jA3k=ijkAi1Aj2Ak3(114)

where the last two equalities represent the expansion of the determinant by row and by

column. Alternatively

det (A) = 1

3!ijklmnAil Ajm Akn (115)

•For an n×nmatrix representing a rank-2 tensor in an nD space, the determinant is:

det (A) = i1···inA1i1. . . Anin=i1···inAi11. . . Ainn=1

n!i1···inj1···jnAi1j1. . . Ainjn(116)

•The inverse of a matrix Arepresenting a rank-2 tensor is:

A−1ij =1

2 det (A)jmn ipq AmpAnq (117)

•The multiplication of a matrix Aby a vector bas deﬁned in linear algebra is:

[Ab]i=Aij bj(118)

4.1 Common Deﬁnitions in Tensor Notation 47

It should be noticed that here we are using matrix notation. The multiplication operation,

according to the symbolic notation of tensors, should be denoted by a dot between the

tensor and the vector, i.e. A·b.12

•The multiplication of two n×nmatrices Aand Bas deﬁned in linear algebra is:

[AB]ik =Aij Bjk (119)

Again, here we are using matrix notation; otherwise a dot should be inserted between the

two matrices.

•The dot product of two vectors is:

A·B=δij AiBj=AiBi(120)

The readers are referred to §2.6.5 for a more general deﬁnition of this type of product

that includes higher rank tensors.

•The cross product of two vectors is:

[A×B]i=ijk AjBk(121)

•The scalar triple product of three vectors is:

A·(B×C) =

A1A2A3

B1B2B3

C1C2C3

=ijk AiBjCk(122)

12The matrix multiplication in matrix notation is equivalent to a dot product operation in tensor

notation.

4.2 Scalar Invariants of Tensors 48

•The vector triple product of three vectors is:

[A×(B×C)]i=ijk klm AjBlCm(123)

4.2 Scalar Invariants of Tensors

•In the following we list and write in tensor notation a number of invariants of low rank

tensors which have special importance due to their widespread applications in vector and

tensor calculus. All These invariants are scalars.

•The value of a scalar (rank-0 tensor), which consists of a magnitude and a sign, is

invariant under coordinate transformation.

•An invariant of a vector (rank-1 tensor) under coordinate transformations is its magni-

tude, i.e. length (the direction is also invariant but it is not scalar!).13

•The main three independent scalar invariants of a rank-2 tensor Aunder change of basis

are:

I= tr (A) = Aii (124)

II = tr A2=Aij Aji (125)

III = tr A3=Aij Aj kAki (126)

•Diﬀerent forms of the three invariants of a rank-2 tensor A, which are also widely used,

are:

I1=I=Aii (127)

13In fact the magnitude alone is invariant under coordinate transformations even for pseudo vectors

because it is a scalar.

4.3 Common Diﬀerential Operations in Tensor Notation 49

I2=1

2I2−II=1

2(AiiAj j −Aij Aji ) (128)

I3= det (A) = 1

3! I3−3I II + 2III =1

3!ijkpq rAipAj qAkr (129)

•The invariants I,II and II I can similarly be deﬁned in terms of the invariants I1,I2

and I3as follow:

I=I1(130)

II =I2

1−2I2(131)

III =I3

1−3I1I2+ 3I3(132)

•Since the determinant of a matrix representing a rank-2 tensor is invariant, then if the

determinant vanishes in one coordinate system it will vanish in all coordinate systems and

vice versa. Consequently, if a rank-2 tensor is invertible in a particular coordinate system,

it is invertible in all coordinate systems.

•Ten joint invariants between two rank-2 tensors, Aand B, can be formed; these are:

tr (A), tr (B), tr (A2), tr (B2), tr (A3), tr (B3), tr (A·B), tr (A2·B), tr (A·B2) and

tr (A2·B2).

4.3 Common Diﬀerential Operations in Tensor Notation

•Here we present the most common diﬀerential operations as deﬁned by tensor notation.

These operations are mostly based on the various types of interaction between the vector

diﬀerential operator nabla ∇with tensors of diﬀerent ranks as well as interaction with

other types of operation like dot and cross products.

4.3.1 Cartesian System 50

• ∇ is essentially a spatial partial diﬀerential operator deﬁned in Cartesian coordinate

systems by:

∇i=∂

∂xi

(133)

The deﬁnition of ∇in some non-Cartesian systems will be given in §4.3.2.

4.3.1 Cartesian System

•The gradient of a diﬀerentiable scalar function of position fis a vector given by:

[∇f]i=∇if=∂f

∂xi

=∂if=f,i (134)

•The gradient of a diﬀerentiable vector function of position A(which is the outer product,

as deﬁned in §2.6.3, between the ∇operator and the vector) is a rank-2 tensor deﬁned

by:

[∇A]ij =∂iAj(135)

•The gradient operation is distributive but not commutative or associative:

∇(f+h) = ∇f+∇h(136)

∇f6=f∇(137)

(∇f)h6=∇(fh) (138)

where fand hare diﬀerentiable scalar functions of position.

4.3.1 Cartesian System 51

•The divergence of a diﬀerentiable vector Ais a scalar given by:

∇ · A=δij

∂Ai

∂xj

=∂Ai

∂xi

=∇iAi=∂iAi=Ai,i (139)

The divergence operation can also be viewed as taking the gradient of the vector followed

by a contraction. Hence, the divergence of a vector is invariant because it is the trace of

a rank-2 tensor.14

•The divergence of a diﬀerentiable rank-2 tensor Ais a vector deﬁned in one of its forms

by:

[∇ · A]i=∂jAji (140)

and in another form by

[∇ · A]j=∂iAji (141)

These two diﬀerent forms can be given, respectively, in symbolic notation by:

∇ · A&∇ · AT(142)

where ATis the transpose of A. More generally, the divergence of a tensor of rank n≥2,

which is a tensor of rank-(n−1), can be deﬁned in several forms, which are diﬀerent in

general, depending on the combination of the contracted indices.

•The divergence operation is distributive but not commutative or associative:

∇ · (A+B) = ∇ · A+∇ · B(143)

∇ · A6=A· ∇ (144)

14It may also be argued that the divergence of a vector is a scalar and hence it is invariant.

4.3.1 Cartesian System 52

∇ · (fA)6=∇f·A(145)

where Aand Bare diﬀerentiable tensor functions of position.

•The curl of a diﬀerentiable vector Ais a vector given by:

[∇ × A]i=ijk

∂Ak

∂xj

=ijk ∇jAk=ijk∂jAk=ij k Ak,j (146)

•The curl operation may be generalized to tensors of rank >1, and hence the curl of a

diﬀerentiable rank-2 tensor Acan be deﬁned as a rank-2 tensor given by:

[∇ × A]ij =imn∂mAnj (147)

•The curl operation is distributive but not commutative or associative:

∇ × (A+B) = ∇ × A+∇ × B(148)

∇ × A6=A× ∇ (149)

∇ × (A×B)6= (∇ × A)×B(150)

•The Laplacian scalar operator, also called the harmonic operator, acting on a diﬀeren-

tiable scalar fis given by:

∆f=∇2f=δij

∂2f

∂xi∂xj

=∂2f

∂xi∂xi

=∇iif=∂ii f=f,ii (151)

•The Laplacian operator acting on a diﬀerentiable vector Ais deﬁned for each component

4.3.2 Other Coordinate Systems 53

of the vector similar to the deﬁnition of the Laplacian acting on a scalar, that is

∇2Ai=∂jj Ai(152)

•The following scalar diﬀerential operator is commonly used in science (e.g. in ﬂuid

dynamics):

A· ∇ =Ai∇i=Ai

∂

∂xi

=Ai∂i(153)

where Ais a vector. As indicated earlier, the order of Aiand ∂ishould be respected.

•The following vector diﬀerential operator also has common applications in science:

[A× ∇]i=ijk Aj∂k(154)

•The diﬀerentiation of a tensor increases its rank by one, by introducing an extra covariant

index, unless it implies a contraction in which case it reduces the rank by one. Therefore

the gradient of a scalar is a vector and the gradient of a vector is a rank-2 tensor (∂iAj),

while the divergence of a vector is a scalar and the divergence of a rank-2 tensor is a vector

(∂jAji or ∂iAji). This may be justiﬁed by the fact that ∇is a vector operator. On the

other hand the Laplacian operator does not change the rank since it is a scalar operator;

hence the Laplacian of a scalar is a scalar and the Laplacian of a vector is a vector.

4.3.2 Other Coordinate Systems

•For completeness, we deﬁne here some diﬀerential operations in the most commonly

used non-Cartesian coordinate systems, namely cylindrical and spherical systems, as well

as general orthogonal coordinate systems.

•We can use indexed generalized coordinates like q1,q2and q3for the cylindrical coor-

dinates (ρ, φ, z) and the spherical coordinates (r, θ, φ). However, for more clarity at this

4.3.2 Other Coordinate Systems 54

level and to follow the more conventional practice, we use the coordinates of these systems

as suﬃxes in place of the indices used in the tensor notation.15

•For the cylindrical system identiﬁed by the coordinates (ρ, φ, z) with an orthonormal

basis vectors eρ,eφand ez:16

The ∇operator is:

∇=eρ∂ρ+eφ

1

ρ∂φ+ez∂z(155)

The Laplacian operator is:

∇2=∂ρρ +1

ρ∂ρ+1

ρ2∂φφ +∂zz (156)

The gradient of a diﬀerentiable scalar fis:

∇f=eρ

∂f

∂ρ +eφ

1

ρ

∂f

∂φ +ez

∂f

∂z (157)

The divergence of a diﬀerentiable vector Ais:

∇ · A=1

ρ∂(ρAρ)

∂ρ +∂Aφ

∂φ +∂(ρAz)

∂z (158)

The curl of a diﬀerentiable vector Ais:

∇ × A=1

ρ

eρρeφez

∂

∂ρ

∂

∂φ

∂

∂z

AρρAφAz

(159)

For plane polar coordinate systems, these operators and operations can be obtained by

dropping the zcomponents or terms from the cylindrical form of the above operators and

15There is another reason that is these are physical components not covariant or contravariant.

16It should be obvious that since ρ, φ and zare speciﬁc coordinates and not variable indices, the

summation convention does not apply.

4.3.2 Other Coordinate Systems 55

operations.

•For the spherical system identiﬁed by the coordinates (r, θ, φ) with an orthonormal basis

vectors er,eθand eφ:17

The ∇operator is:

∇=er∂r+eθ

1

r∂θ+eφ

1

rsin θ∂φ(160)

The Laplacian operator is:

∇2=∂rr +2

r∂r+1

r2∂θθ +cos θ

r2sin θ∂θ+1

r2sin2θ∂φφ (161)

The gradient of a diﬀerentiable scalar fis:

∇f=er

∂f

∂r +eθ

1

r

∂f

∂θ +eφ

1

rsin θ

∂f

∂φ (162)

The divergence of a diﬀerentiable vector Ais:

∇ · A=1

r2sin θsin θ∂(r2Ar)

∂r +r∂(sin θAθ)

∂θ +r∂Aφ

∂φ (163)

The curl of a diﬀerentiable vector Ais:

∇ × A=1

r2sin θ

erreθrsin θeφ

∂

∂r

∂

∂θ

∂

∂φ

ArrAθrsin θAφ

(164)

•For a general orthogonal system in a 3D space identiﬁed by the coordinates (u1, u2, u3)

with unit basis vectors u1,u2and u3and scale factors h1, h2and h3where hi=

∂r

∂uiand

ris the position vector:

17Again, the summation convention does not apply to r, θ and φ.

4.4 Common Identities in Vector and Tensor Notation 56

The ∇operator is:

∇=u1

h1

∂

∂u1

+u2

h2

∂

∂u2

+u3

h3

∂

∂u3

(165)

The Laplacian operator is:

∇2=1

h1h2h3∂

∂u1h2h3

h1

∂

∂u1+∂

∂u2h1h3

h2

∂

∂u2+∂

∂u3h1h2

h3

∂

∂u3 (166)

The gradient of a diﬀerentiable scalar fis:

∇f=u1

h1

∂f

∂u1

+u2

h2

∂f

∂u2

+u3

h3

∂f

∂u3

(167)

The divergence of a diﬀerentiable vector Ais:

∇ · A=1

h1h2h3∂

∂u1

(h2h3A1) + ∂

∂u2

(h1h3A2) + ∂

∂u3

(h1h2A3)(168)

The curl of a diﬀerentiable vector Ais:

∇ × A=1

h1h2h3

h1u1h2u2h3u3

∂

∂u1

∂

∂u2

∂

∂u3

h1A1h2A2h3A3

(169)

4.4 Common Identities in Vector and Tensor Notation

•Here we present some of the widely used identities of vector calculus in the traditional

vector notation and in its equivalent tensor notation. In the following bullet points, f

and hare diﬀerentiable scalar ﬁelds; A,B,Cand Dare diﬀerentiable vector ﬁelds; and

r=xieiis the position vector.

4.4 Common Identities in Vector and Tensor Notation 57

•

∇ · r=n

m(170)

∂ixi=n

where nis the space dimension.

•

∇ × r=0

m(171)

ijk ∂jxk= 0

•

∇(a·r) = a

m(172)

∂i(ajxj) = ai

where ais a constant vector.

•

∇ · (∇f) = ∇2f

m(173)

∂i(∂if) = ∂iif

4.4 Common Identities in Vector and Tensor Notation 58

•

∇ · (∇ × A)=0

m(174)

ijk ∂i∂jAk= 0

•

∇ × (∇f) = 0

m(175)

ijk ∂j∂kf= 0

•

∇(fh) = f∇h+h∇f

m(176)

∂i(fh) = f ∂ih+h∂if

•

∇ · (fA) = f∇ · A+A· ∇f

m(177)

∂i(fAi) = f ∂iAi+Ai∂if

4.4 Common Identities in Vector and Tensor Notation 59

•

∇ × (fA) = f∇ × A+∇f×A

m(178)

ijk ∂j(f Ak) = fijk∂jAk+ij k (∂jf)Ak

•

A·(B×C) = C·(A×B) = B·(C×A)

m m (179)

ijk AiBjCk=kij CkAiBj=j kiBjCkAi

•

A×(B×C) = B(A·C)−C(A·B)

m(180)

ijk Ajklm BlCm=Bi(AmCm)−Ci(AlBl)

•

A×(∇ × B)=(∇B)·A−A· ∇B

m(181)

ijk klm Aj∂lBm= (∂iBm)Am−Al(∂lBi)

4.4 Common Identities in Vector and Tensor Notation 60

•

∇ × (∇ × A) = ∇(∇ · A)− ∇2A

m(182)

ijk klm ∂j∂lAm=∂i(∂mAm)−∂ll Ai

•

∇(A·B) = A×(∇ × B) + B×(∇ × A)+(A· ∇)B+ (B· ∇)A

m(183)

∂i(AmBm) = ijk Aj(klm ∂lBm) + ijkBj(klm∂lAm)+(Al∂l)Bi+ (Bl∂l)Ai

•

∇ · (A×B) = B·(∇ × A)−A·(∇ × B)

m(184)

∂i(ijk AjBk) = Bk(kij ∂iAj)−Aj(j ik∂iBk)

•

∇ × (A×B)=(B· ∇)A+ (∇ · B)A−(∇ · A)B−(A· ∇)B

m(185)

ijk klm ∂j(AlBm)=(Bm∂m)Ai+ (∂mBm)Ai−(∂jAj)Bi−(Aj∂j)Bi

4.4 Common Identities in Vector and Tensor Notation 61

•

(A×B)·(C×D) =

A·C A ·D

B·C B ·D

m(186)

ijk AjBkilmClDm= (AlCl) (BmDm)−(AmDm) (BlCl)

•

(A×B)×(C×D)=[D·(A×B)] C−[C·(A×B)] D

m(187)

ijk jmnAmBnkpq CpDq= (qmnDqAmBn)Ci−(pmn CpAmBn)Di

•In vector and tensor notations, the condition for a vector ﬁeld Ato be solenoidal is:

∇ · A= 0

m(188)

∂iAi= 0

•In vector and tensor notations, the condition for a vector ﬁeld Ato be irrotational is:

∇ × A=0

m(189)

ijk ∂jAk= 0

4.5 Integral Theorems in Tensor Notation 62

4.5 Integral Theorems in Tensor Notation

•The divergence theorem for a diﬀerentiable vector ﬁeld Ain vector and tensor notation

is:

ZZZV

∇ · Adτ =ZZS

A·ndσ

m(190)

ZV

∂iAidτ =ZS

Ainidσ

where Vis a bounded region in an nD space enclosed by a generalized surface S,dτ and

dσ are generalized volume and surface elements respectively, nand niare unit normal to

the surface and its ith component respectively, and the index iranges over 1, . . . , n.

•The divergence theorem for a diﬀerentiable rank-2 tensor ﬁeld Ain tensor notation for

the ﬁrst index is given by:

ZV

∂iAildτ =ZS

Ailnidσ (191)

•The divergence theorem for diﬀerentiable tensor ﬁelds of higher ranks Ain tensor nota-

tion for the index kis:

ZV

∂kAij...k...m dτ =ZS

Aij...k...m nkdσ (192)

•Stokes theorem for a diﬀerentiable vector ﬁeld Ain vector and tensor notation is:

ZZS

(∇ × A)·ndσ =ZC

A·dr

m(193)

ZS

ijk ∂jAknidσ =ZC

Aidxi

where Cstands for the perimeter of the surface Sand dris the vector element tangent to

4.6 Examples of Using Tensor Techniques to Prove Identities 63

the perimeter.

•Stokes theorem for a diﬀerentiable rank-2 tensor ﬁeld Ain tensor notation for the ﬁrst

index is:

ZS

ijk ∂jAkl nidσ =ZC

Aildxi(194)

•Stokes theorem for diﬀerentiable tensor ﬁelds of higher ranks Ain tensor notation for

the index kis:

ZS

ijk ∂jAlm...k...n nidσ =ZC

Alm...k...ndxk(195)

4.6 Examples of Using Tensor Techniques to Prove Identities

• ∇ · r=n:

∇ · r=∂ixi(Eq. 139)

=δii (Eq. 80)

=n(Eq. 80)

(196)

• ∇ × r=0:

[∇ × r]i=ijk ∂jxk(Eq. 146)

=ijk δkj (Eq. 79)

=ijj (Eq. 76)

= 0 (Eq. 71)

(197)

Since iis a free index the identity is proved for all components.

4.6 Examples of Using Tensor Techniques to Prove Identities 64

• ∇ (a·r) = a:

[∇(a·r)]i=∂i(ajxj) (Eqs. 134 &120)

=aj∂ixj+xj∂iaj(product rule)

=aj∂ixj(ajis constant)

=ajδji (Eq. 79)

=ai(Eq. 76)

= [a]i(deﬁnition of index)

(198)

Since iis a free index the identity is proved for all components.

• ∇ · (∇f) = ∇2f:

∇ · (∇f) = ∂i[∇f]i(Eq. 139)

=∂i(∂if) (Eq. 134)

=∂i∂if(rules of diﬀerentiation)

=∂iif(deﬁnition of 2nd derivative)

=∇2f(Eq. 151)

(199)

4.6 Examples of Using Tensor Techniques to Prove Identities 65

• ∇ · (∇ × A) = 0:

∇ · (∇ × A) = ∂i[∇ × A]i(Eq. 139)

=∂i(ijk ∂jAk) (Eq. 146)

=ijk ∂i∂jAk(∂not acting on )

=ijk ∂j∂iAk(continuity condition)

=−jik ∂j∂iAk(Eq. 84)

=−ijk ∂i∂jAk(relabeling dummy indices iand j)

= 0 (since ijk ∂i∂jAk=−ij k∂i∂jAk)

(200)

This can also be concluded from line three by arguing that: since by the continuity con-

dition ∂iand ∂jcan change their order with no change in the value of the term while a

corresponding change of the order of iand jin ijk results in a sign change, we see that

each term in the sum has its own negative and hence the terms add up to zero (see Eq.

94).

• ∇ × (∇f) = 0:

[∇ × (∇f)]i=ijk ∂j[∇f]k(Eq. 146)

=ijk ∂j(∂kf) (Eq. 134)

=ijk ∂j∂kf(rules of diﬀerentiation)

=ijk ∂k∂jf(continuity condition)

=−ikj ∂k∂jf(Eq. 84)

=−ijk ∂j∂kf(relabeling dummy indices jand k)

= 0 (since ij k∂j∂kf=−ijk ∂j∂kf)

(201)

This can also be concluded from line three by a similar argument to the one given in the

4.6 Examples of Using Tensor Techniques to Prove Identities 66

previous point. Because [∇ × (∇f)]iis an arbitrary component, then each component is

zero.

• ∇ (fh) = f∇h+h∇f:

[∇(fh)]i=∂i(f h) (Eq. 134)

=f∂ih+h∂if(product rule)

= [f∇h]i+ [h∇f]i(Eq. 134)

= [f∇h+h∇f]i(Eq. 16)

(202)

Because iis a free index the identity is proved for all components.

• ∇ · (fA) = f∇ · A+A· ∇f:

∇ · (fA) = ∂i[fA]i(Eq. 139)

=∂i(fAi) (deﬁnition of index)

=f∂iAi+Ai∂if(product rule)

=f∇ · A+A· ∇f(Eqs. 139 &153)

(203)

• ∇ × (fA) = f∇ × A+∇f×A:

[∇ × (fA)]i=ijk ∂j[fA]k(Eq. 146)

=ijk ∂j(f Ak) (deﬁnition of index)

=fijk∂jAk+ijk (∂jf)Ak(product rule & commutativity)

=fijk∂jAk+ijk [∇f]jAk(Eq. 134)

= [f∇ × A]i+ [∇f×A]i(Eqs. 146 &121)

= [f∇ × A+∇f×A]i(Eq. 16)

(204)

Because iis a free index the identity is proved for all components.

4.6 Examples of Using Tensor Techniques to Prove Identities 67

•A·(B×C) = C·(A×B) = B·(C×A):

A·(B×C) = ijk AiBjCk(Eq. 122)

=kij AiBjCk(Eq. 84)

=kij CkAiBj(commutativity)

=C·(A×B) (Eq. 122)

=jki AiBjCk(Eq. 84)

=jki BjCkAi(commutativity)

=B·(C×A) (Eq. 122)

(205)

The negative permutations of these identities can be similarly obtained and proved by

changing the order of the vectors in the cross products which results in a sign change.

4.6 Examples of Using Tensor Techniques to Prove Identities 68

•A×(B×C) = B(A·C)−C(A·B):

[A×(B×C)]i=ijk Aj[B×C]k(Eq. 121)

=ijk Ajklm BlCm(Eq. 121)

=ijk klm AjBlCm(commutativity)

=ijk lmk AjBlCm(Eq. 84)

= (δilδj m −δimδj l)AjBlCm(Eq. 102)

=δilδj mAjBlCm−δim δjlAjBlCm(distributivity)

= (δilBl) (δj mAjCm)−(δim Cm) (δjlAjBl) (commutativity and grouping)

=Bi(AmCm)−Ci(AlBl) (Eq. 76)

=Bi(A·C)−Ci(A·B) (Eq. 120)

= [B(A·C)]i−[C(A·B)]i(deﬁnition of index)

= [B(A·C)−C(A·B)]i(Eq. 16)

(206)

Because iis a free index the identity is proved for all components. Other variants of this

identity [e.g. (A×B)×C] can be obtained and proved similarly by changing the order

of the factors in the external cross product with adding a minus sign.

4.6 Examples of Using Tensor Techniques to Prove Identities 69

•A×(∇ × B) = (∇B)·A−A· ∇B:

[A×(∇ × B)]i=ijk Aj[∇ × B]k(Eq. 121)

=ijk Ajklm ∂lBm(Eq. 146)

=ijk klm Aj∂lBm(commutativity)

=ijk lmk Aj∂lBm(Eq. 84)

= (δilδj m −δimδj l)Aj∂lBm(Eq. 102)

=δilδj mAj∂lBm−δim δjlAj∂lBm(distributivity)

=Am∂iBm−Al∂lBi(Eq. 76)

= (∂iBm)Am−Al(∂lBi) (commutativity & grouping)

= [(∇B)·A]i−[A· ∇B]i(Eq. 135 &§2.6.5)

= [(∇B)·A−A· ∇B]i(Eq. 16)

(207)

Because iis a free index the identity is proved for all components.

4.6 Examples of Using Tensor Techniques to Prove Identities 70

• ∇ × (∇ × A) = ∇(∇ · A)− ∇2A:

[∇ × (∇ × A)]i=ijk ∂j[∇ × A]k(Eq. 146)

=ijk ∂j(klm ∂lAm) (Eq. 146)

=ijk klm ∂j(∂lAm) (∂not acting on )

=ijk lmk ∂j∂lAm(Eq. 84 & deﬁnition of derivative)

= (δilδj m −δimδj l)∂j∂lAm(Eq. 102)

=δilδj m∂j∂lAm−δim δjl∂j∂lAm(distributivity)

=∂m∂iAm−∂l∂lAi(Eq. 76)

=∂i(∂mAm)−∂llAi(∂shift, grouping & Eq. 4)

= [∇(∇ · A)]i−∇2Ai(Eqs. 139,134 &152)

=∇(∇ · A)− ∇2Ai(Eqs. 16)

(208)

Because iis a free index the identity is proved for all components. This identity can also

be considered as an instance of the identity before the last one, observing that in the

second term on the right hand side the Laplacian should precede the vector, and hence no

independent proof is required.

• ∇ (A·B) = A×(∇ × B) + B×(∇ × A)+(A· ∇)B+ (B· ∇)A:

4.6 Examples of Using Tensor Techniques to Prove Identities 71

We start from the right hand side and end with the left hand side

[A×(∇ × B) + B×(∇ × A)+(A· ∇)B+ (B· ∇)A]i=

[A×(∇ × B)]i+ [B×(∇ × A)]i+ [(A· ∇)B]i+ [(B· ∇)A]i= (Eq. 16)

ijk Aj[∇ × B]k+ijk Bj[∇ × A]k+ (Al∂l)Bi+ (Bl∂l)Ai= (Eqs. 121,139 & indexing)

ijk Aj(klm ∂lBm) + ijk Bj(klm ∂lAm)+(Al∂l)Bi+ (Bl∂l)Ai= (Eq. 146)

ijk klm Aj∂lBm+ijk klm Bj∂lAm+ (Al∂l)Bi+ (Bl∂l)Ai= (commutativity)

ijk lmk Aj∂lBm+ijk lmk Bj∂lAm+ (Al∂l)Bi+ (Bl∂l)Ai= (Eq. 84)

(δilδj m −δimδj l)Aj∂lBm+ (δil δj m −δimδj l)Bj∂lAm+ (Al∂l)Bi+ (Bl∂l)Ai= (Eq. 102) (209)

(δilδj mAj∂lBm−δim δjl Aj∂lBm)+(δil δjmBj∂lAm−δim δjl Bj∂lAm)+(Al∂l)Bi+ (Bl∂l)Ai= (distributivity)

δilδj mAj∂lBm−Al∂lBi+δil δjm Bj∂lAm−Bl∂lAi+ (Al∂l)Bi+ (Bl∂l)Ai= (Eq. 76)

δilδj mAj∂lBm−(Al∂l)Bi+δil δjm Bj∂lAm−(Bl∂l)Ai+ (Al∂l)Bi+ (Bl∂l)Ai= (grouping)

δilδj mAj∂lBm+δil δjm Bj∂lAm= (cancellation)

Am∂iBm+Bm∂iAm= (Eq. 76)

∂i(AmBm) = (product rule)

= [∇(A·B)]i(Eqs. 134 &139)

Because iis a free index the identity is proved for all components.

4.6 Examples of Using Tensor Techniques to Prove Identities 72

• ∇ · (A×B) = B·(∇ × A)−A·(∇ × B):

∇ · (A×B) = ∂i[A×B]i(Eq. 139)

=∂i(ijk AjBk) (Eq. 121)

=ijk ∂i(AjBk) (∂not acting on )

=ijk (Bk∂iAj+Aj∂iBk) (product rule)

=ijk Bk∂iAj+ijkAj∂iBk(distributivity)

=kij Bk∂iAj−jik Aj∂iBk(Eq. 84)

=Bk(kij ∂iAj)−Aj(jik ∂iBk) (commutativity & grouping)

=Bk[∇ × A]k−Aj[∇ × B]j(Eq. 146)

=B·(∇ × A)−A·(∇ × B) (Eq. 120)

(210)

4.6 Examples of Using Tensor Techniques to Prove Identities 73

• ∇ × (A×B) = (B· ∇)A+ (∇ · B)A−(∇ · A)B−(A· ∇)B:

[∇ × (A×B)]i=ijk ∂j[A×B]k(Eq. 146)

=ijk ∂j(klm AlBm) (Eq. 121)

=ijk klm ∂j(AlBm) (∂not acting on )

=ijk klm (Bm∂jAl+Al∂jBm) (product rule)

=ijk lmk (Bm∂jAl+Al∂jBm) (Eq. 84)

= (δilδj m −δimδj l) (Bm∂jAl+Al∂jBm) (Eq. 102)

=δilδj mBm∂jAl+δil δjmAl∂jBm−δim δjl Bm∂jAl−δim δjl Al∂jBm(distributivity)

=Bm∂mAi+Ai∂mBm−Bi∂jAj−Aj∂jBi(Eq. 76)

= (Bm∂m)Ai+ (∂mBm)Ai−(∂jAj)Bi−(Aj∂j)Bi(grouping)

= [(B· ∇)A]i+ [(∇ · B)A]i−[(∇ · A)B]i−[(A· ∇)B]i(Eqs. 153 &139)

= [(B· ∇)A+ (∇ · B)A−(∇ · A)B−(A· ∇)B]i(Eq. 16)

(211)

Because iis a free index the identity is proved for all components.

4.6 Examples of Using Tensor Techniques to Prove Identities 74

•(A×B)·(C×D) =

A·C A ·D

B·C B ·D

:

(A×B)·(C×D) = [A×B]i[C×D]i(Eq. 120)

=ijk AjBkilmClDm(Eq. 121)

=ijk ilmAjBkClDm(commutativity)

= (δjl δkm −δjmδkl )AjBkClDm(Eqs. 84 &102)

=δjl δkmAjBkClDm−δjm δkl AjBkClDm(distributivity)

= (δjl AjCl) (δkmBkDm)−(δjm AjDm) (δkl BkCl) (commutativity & grouping)

= (AlCl) (BmDm)−(AmDm) (BlCl) (Eq. 76)

= (A·C) (B·D)−(A·D) (B·C) (Eq. 120)

=

A·C A ·D

B·C B ·D

(deﬁnition of determinant)

(212)

4.6 Examples of Using Tensor Techniques to Prove Identities 75

•(A×B)×(C×D) = [D·(A×B)] C−[C·(A×B)] D:

[(A×B)×(C×D)]i=ijk [A×B]j[C×D]k(Eq. 121)

=ijk jmnAmBnkpq CpDq(Eq. 121)

=ijk kpq j mnAmBnCpDq(commutativity)

=ijk pqk j mnAmBnCpDq(Eq. 84)

= (δipδj q −δiqδjp )jmn AmBnCpDq(Eq. 102)

= (δipδj qjmn −δiq δjpj mn)AmBnCpDq(distributivity)

= (δipqmn −δiqpmn )AmBnCpDq(Eq. 76)

=δipqmnAmBnCpDq−δiq pmnAmBnCpDq(distributivity)

=qmnAmBnCiDq−pmnAmBnCpDi(Eq. 76)

=qmnDqAmBnCi−pmnCpAmBnDi(commutativity)

= (qmnDqAmBn)Ci−(pmnCpAmBn)Di(grouping)

= [D·(A×B)] Ci−[C·(A×B)] Di(Eq. 122)

= [[D·(A×B)] C]i−[[C·(A×B)] D]i(deﬁnition of index)

= [[D·(A×B)] C−[C·(A×B)] D]i(Eq. 16)

(213)

Because iis a free index the identity is proved for all components.

5 METRIC TENSOR 76

5 Metric Tensor

•This is a rank-2 tensor which may also be called the fundamental tensor.

•The main purpose of the metric tensor is to generalize the concept of distance to gen-

eral curvilinear coordinate frames and maintain the invariance of distance in diﬀerent

coordinate systems.

•In orthonormal Cartesian coordinate systems the distance element squared, (ds)2, be-

tween two inﬁnitesimally neighboring points in space, one with coordinates xiand the

other with coordinates xi+dxi, is given by

(ds)2=dxidxi=δij dxidxj(214)

This deﬁnition of distance is the key to introducing a rank-2 tensor, gij, called the metric

tensor which, for a general coordinate system, is deﬁned by

(ds)2=gij dxidxj(215)

The metric tensor has also a contravariant form, i.e. gij .

•The components of the metric tensor are given by:

gij =Ei·Ej&gij =Ei·Ej(216)

where the indexed Eare the covariant and contravariant basis vectors as deﬁned in §2.5.1.

•The mixed type metric tensor is given by:

gi

j=Ei·Ej=δi

j&gj

i=Ei·Ej=δj

i(217)

and hence it is the same as the unity tensor.

5 METRIC TENSOR 77

•For a coordinate system in which the metric tensor can be cast in a diagonal form where

the diagonal elements are ±1 the metric is called ﬂat.

•For Cartesian coordinate systems, which are orthonormal ﬂat-space systems, we have

gij =δij =gij =δij (218)

•The metric tensor is symmetric, that is

gij =gji &gij =gji (219)

•The contravariant metric tensor is used for raising indices of covariant tensors and the

covariant metric tensor is used for lowering indices of contravariant tensors, e.g.

Ai=gij AjAi=gij Aj(220)

where the metric tensor acts, like a Kronecker delta, as an index replacement operator.

Hence, any tensor can be cast into a covariant or a contravariant form, as well as a mixed

form. However, the order of the indices should be respected in this process, e.g.

Ai

j=gjk Aik 6=Ai

j=gjk Aki (221)

Some authors insert dots (e.g. A·i

j) to remove any ambiguity about the order of the indices.

•The covariant and contravariant metric tensors are inverses of each other, that is

[gij ] = gij −1&gij = [gij ]−1(222)

Hence

gikgkj =δi

j&gikgkj =δj

i(223)

5 METRIC TENSOR 78

•It is common to reserve the “metric tensor” to the covariant form and call the con-

travariant form, which is its inverse, the “associate” or “conjugate” or “reciprocal” metric

tensor.

•As a tensor, the metric has a signiﬁcance regardless of any coordinate system although

it requires a coordinate system to be represented in a speciﬁc form.

•For orthogonal coordinate systems the metric tensor is diagonal, i.e. gij =gij = 0 for

i6=j.

•For ﬂat-space orthonormal Cartesian coordinate systems in a 3D space, the metric tensor

is given by:

[gij ] = [δij ] =

1 0 0

0 1 0

0 0 1

=δij =gij (224)

•For cylindrical coordinate systems with coordinates (ρ, φ, z), the metric tensor is given

by:

[gij ] =

100

0ρ20

001

&gij =

1 0 0

01

ρ20

0 0 1

(225)

•For spherical coordinate systems with coordinates (r, θ, φ), the metric tensor is given by:

[gij ] =

1 0 0

0r20

0 0 r2sin2θ

&gij =

1 0 0

01

r20

0 0 1

r2sin2θ

(226)

6 COVARIANT DIFFERENTIATION 79

6 Covariant Diﬀerentiation

•The ordinary derivative of a tensor is not a tensor in general. The objective of covariant

diﬀerentiation is to ensure the invariance of derivative (i.e. being a tensor) in general

coordinate systems, and this results in applying more sophisticated rules using Christof-

fel symbols where diﬀerent diﬀerentiation rules for covariant and contravariant indices

apply. The resulting covariant derivative is a tensor which is one rank higher than the

diﬀerentiated tensor.

•Christoﬀel symbol of the second kind is deﬁned by:

k

ij =gkl

2∂gil

∂xj+∂gjl

∂xi−∂gij

∂xl(227)

where the indexed gis the metric tensor in its contravariant and covariant forms with

implied summation over l. It is noteworthy that Christoﬀel symbols are not tensors.

•The Christoﬀel symbols of the second kind are symmetric in their two lower indices:

k

ij =k

ji (228)

•For Cartesian coordinate systems, the Christoﬀel symbols are zero for all the values of

indices.

•For cylindrical coordinate systems (ρ, φ, z), the Christoﬀel symbols are zero for all the

values of indices except:

1

22=−ρ(229)

2

12=2

21=1

ρ

where (1,2,3) stand for (ρ, φ, z).