Bojana Rosic

University of Twente | UT

Spatial symmetries and invariances play an important role in the behaviour of materials and should be respected in the description and modelling of material properties. The focus here is the class of physically symmetric and positive definite tensors, as they appear often in the description of materials, and one wants tobe able to prescribe certain...

We propose a novel scale-invariant version of the mean and variance multi-level Monte Carlo estimate. The computation cost across grid levels is optimised using a normalized error based on t-statistics. By doing so, the algorithm achieves convergence independent of the physical scale at which the estimate is computed. The effectiveness of this algo...

In this work, we propose Bayesian parameter estimation of a nonlinear mechanics based model describing the behaviour of mortar subjected to double shear test with externally bonded carbon fibre reinforced polymer (CFRP) plates. With the Bayesian approach, it is possible to identify mechanical material parameters of different phases of the mortar me...

To incorporate sparsity knowledge as well as measurement uncertainties in the traditional long short-term memory (LSTM) neural networks, an efficient relevance vector machine algorithm is introduced to the network architecture. The proposed scheme automatically determines relevant neural connections and adapts accordingly, in contrast to the classi...

Within the realm of isogeometric analysis, isogeometric collocation has been driven by the attempt to minimize the cost of quadrature associated with higher-order discretizations, with the goal of achieving higher-order accuracy at low computational cost. While the first applications of isogeometric collocation have mainly concerned linear problems...

Inhalt CFD 3D CFD Simulations of Snow Accumulation in the Wheel House of a Vehicle at different Driving Speeds . . . . . . .1 Octree Meshing Technology for External Aerodynamic CFD Applications. . . . . . . . . . 21 Two-phase water evaporation MESHFREE CFD method for wet automotive component dry-out time prediction. . . . . . . . . . . .33 Next Gen...

The resistance to bending of continuous fibre-reinforced thermoplastic composites at processing temperature is an important predictor of wrinkle formation during the stamp forming process. This resistance can be quantified with experiments and approximated with models. However, current models are validated with one, or at most two, thermoplastic co...

To incorporate prior knowledge as well as measurement uncertainties in the traditional long short term memory (LSTM) neural networks, an efficient sparse Bayesian training algorithm is introduced to the network architecture. The proposed scheme automatically determines relevant neural connections and adapts accordingly, in contrast to the classical...

Forming simulations are a cost-effective solution to mitigate process-induced defects. The models developed to simulate the forming process require material property data for the dominant deformation mechanisms: intra-ply shear, bending, and inter-ply friction. These mechanisms are considered independent, and material property data has to be derive...

Phase-field modeling of fracture has gained popularity within the last decade due to the flexibility of the related computational framework in simulating three-dimensional arbitrarily complicated fracture processes. However, the numerical predictions are greatly affected by the presence of uncertainties in the mechanical properties of the material...

A finite element model of a tapered tensile specimen with a hardness transition zone in the gauge section and a varying width parameter is used for creating corresponding solution snapshots. Subsequently, a long short-term memory (LSTM) recurrent neural network (RNN) is trained on the selected snapshots, providing a parametrized solution model for...

Multi-scale processes governed on each scale by separate principles for evolution or equilibrium are coupled by matching the stored energy and dissipation in line with the Hill-Mandel principle. We are interested in cementitious materials, and consider here the macro- and meso-scale behaviour of such a material. The accurate representations of stor...

The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The pa...

The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore, identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The p...

To evaluate the cyclic behavior under different loading conditions using the kinematic and isotropic hardening theory of steel, a Chaboche viscoplastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement errors an...

The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore, identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The p...

In this paper we propose a Bayesian method as a numerical way to correct and stabilise projection-based reduced order models (ROM) in computational fluid dynamics problems. The approach is of hybrid type, and consists of the classical proper orthogonal decomposition driven Galerkin projection of the laminar part of the governing equations, and Baye...

In this paper physical multi-scale processes governed by their own principles for evolution or equilibrium on each scale are coupled by matching the stored and dissipated energy, in line with the Hill-Mandel principle. In our view the correct representations of stored and dissipated energy is essential to the representation irreversible material be...

In this paper we propose a Bayesian method as a numerical way to correct and stabilise projection-based reduced order models (ROM) in computational fluid dynamics problems. The approach is of hybrid type, and consists of the classical proper orthogonal decomposition driven Galerkin projection of the laminar part of the governing equations, and Baye...

In this paper is proposed a novel incremental iterative Gauss-Newton-Markov-Kalman filter method for state estimation of dynamic models given noisy measurements. The mathematical formulation of the proposed filter is based on the construction of an optimal nonlinear map between the observable and parameter (state) spaces via a convergent sequence o...

To evaluate the cyclic behavior under different loading conditions using the kinematic and isotropic hardening theory of steel, a Chaboche viscoplastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement errors an...

In this contribution, several case studies with data uncertainties are presented which have been performed in individual projects as part of the DFG (German Research Foundation) Priority Programme SPP 1886 “Polymorphic uncertainty modelling for the numerical design of structures.” In all case studies numerical models with uncertainties are derived...

This paper considers Bayesian identification of macroscopic bone material characteristics given digital image correlation (DIC) data. As the evaluation of the full Bayesian posterior distribution is known to be computationally intense, here we consider the approximate estimation in a Newton‐like manner by using the theory of conditional expectation...

Uncertainty of random variables is commonly characterized from measurement data. In practice, data might be insufficient in order to obtain an accurate probability model. In this work, we assume that the type of distribution of the considered random variable is known a priori, and use a hierarchical parametric probability box (p‐box) – which is a s...

Uncertainty of random variables is commonly characterized from measurement data. In practice, data might be insufficient in order to obtain an accurate probability model. In this work, we assume that the type of distribution of the considered random variable is known a priori, and use a hierarchical parametric probability box (p-box) -- which is a...

In this work we present an upscaling technique for multi-scale computations based on a stochastic model calibration technique. We consider a coarse scale continuum material model described in the framework of generalized standard materials. The model parameters are considered uncertain, and are determined in a Bayesian framework for the given fine...

In this work we present an upscaling technique for multi-scale computations based on a stochastic model calibration technique. We consider a coarse scale continuum material model described in the framework of generalised standard materials. The model parameters are considered uncertain in this approach, and are approximated using random variables....

In this work we present an upscaling technique for multi-scale computations based on random microstructures modelled as realisations of lognormally distributed random fields, or described by randomly distributed inclusions in a homogeneous matrix. Their corresponding coarse-scale model parameters are considered as uncertain, and are approximated by...

The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its...

We propose a probabilistic strategy to upscale the material spatial variability from fine to coarse scale. To implement this idea in a numerical framework, we consider the coarse-scale as stochastic, i. e. its material properties are considered uncertain and modeled as random variables/fields. Numerical examples are shown to demonstrate the applica...

The article aims at detecting and quantifying early structural damages using deterministic and probabilistic model updating techniques. To achieve this purpose, local information in a form of optical strain measurement is employed. The strategy consists in updating physical parameters associated to damages, such as Young’s modulus, in order to mini...

When a mathematical or computational model is used to analyse some system, it is usual that some parameters resp.\ functions or fields in the model are not known, and hence uncertain. These parametric quantities are then identified by actual observations of the response of the real system. In a probabilistic setting, Bayes's theory is the proper ma...

This paper investigates the Bayesian process of identifying unknown model parameters given prior information and a set of noisy measurement data. There are two approaches being adopted in this research: one that uses the classical formula for measures and probability densities and one that leaves the underlying measure unchanged and updates the rel...

In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various a...

In a Bayesian setting, inverse problems and uncertainty quantification (UQ) --- the propagation of uncertainty through a computational (forward) model --- are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, v...

Two classical civil engineering inverse problems are considered. The first deals with the determination of dynamic moving loads applied to a reinforced concrete beam. The second one corresponds to the monitoring and the damage assessment. The concrete damage due to overloading is modeled by a loss of the concrete Young’ modulus, whereas the steel b...

The paper deals with the propagation of uncertainty in input parameters through the aircraft model in clean cruise configuration triggered by the elevator pulse. Assuming aerodynamic coefficients as random variables and processes, the evolution of uncertainties in the aircraft state is estimated with the help of efficient nonintrusive procedures-st...

The procedure for reuse of finite element method (FEM) programs for heat transfer and structure analysis to solve advanced thermo-mechanical problems is presented as powerful algorithm applicable for coupling of other physical fields (magnetic, fluid flow, etc.). In this case, nonlinear Block-Gauss-Seidel partitioned algorithm strongly couples the...

This paper is concerned with the modelling and analysis of the uncertainties which come along with the new technologies of the novel high lift aircraft design. The uncertainty analysis is performed for a deterministic preliminary reference design of an appropriate low noise cruise efficient civil aircraft which enables short take-off and landing as...

The paper deals with the propagation of uncertainty in input parameters through the aircraft model in clean cruise configuration triggered by the elevator pulse. Assuming aerodynamic coefficients as random variables and processes, the evolution of uncertainties in the aircraft state is estimated with the help of efficient nonintrusive procedures—st...

In this paper the irreversible behaviour of solids and structures in terms of rate-independent elastoplastic constitutive models in the presence of uncertainty in both material description and loading is studied. The mathematical background in convex analysis of deterministic elastoplasticity is extended to the stochastic domain, and numerical algo...

"ECCOMAS PhD Olympiad 2013"

We present a sampling-free implementation of a linear Bayesian filter based on a square root formulation. It employs spectral series expansions of the involved random variables, one such example being Wiener’s polynomial chaos. The method is compared to several related methods, as well as a full Bayesian update, on a simple scalar example. Addition...

Parameter identification problems are formulated in a probabilistic language, where the randomness reflects the uncertainty about the knowledge of the true values. This setting allows conceptually easily to incorporate new information, e.g. through a measurement, by connecting it to Bayes's theorem. The unknown quantity is modelled as a (may be hig...

This paper presents the parameter identification in a Bayesian setting for the elastoplastic problem, mathematically speaking the variational inequality of a second kind. The inverse problem is formulated in a probabilistic manner in which unknown quantities are embedded in a form of the probability distributions reflecting their uncertainty. With...

The prediction of thermo-mechanical behaviour of heterogeneous materials such as heat and moisture transport is strongly influenced by the uncertainty in parameters. Such materials occur e.g. in historic buildings, and the durability assessment of these therefore needs a reliable and probabilistic simulation of transport processes, which is related...

We present a sampling-free implementation of a linear Bayesian filter. It is based on spectral series expansions of the involved random variables, one such example beingWiener’s polynomial chaos. The method is applied to a combined state and parameter estimation problem for a chaotic system, the well-known Lorenz-63 model. We compare it to the ense...

We present a fully deterministic approach to a probabilistic interpretation of inverse problems in which unknown quantities are represented by random fields or processes, described by possibly non-Gaussian distributions. The description of the introduced random fields is given in a “white noise” framework, which enables us to solve the stochastic f...

We present a fully deterministic method to compute sequential updates for stochastic state estimates of dynamic models from noisy measurements. It does not need any assumptions about the type of distribution for either data or measurement — in particular it does not have to assume any of them as Gaussian. It is based on a polynomial chaos expansion...

Computational uncertainty quantification in a probabilistic setting is a special case of a parametric problem. Parameter dependent state vectors lead via association to a linear operator to analogues of covariance, its spectral decomposition, and the associated Karhunen-Loève expansion. From this, one obtains a generalised tensor representation. Th...

The usual mathematical method to represent uncertain quantities, for example the state of a dynamical system with uncertain initial conditions, are random variables (RVs). In many problems the space of elementary events Ω, on which the RVs are defined as functions of these events, is not concretely accessible, so that the usual idea of a function (...

The prediction of thermo-mechanical behaviour of heterogeneous materials such as heat and moisture transport is strongly influenced by the uncertainty in parameters. Such materials occur e.g. in historic buildings, and the durability assessment of these therefore needs a reliable and probabilistic simulation of transport processes, which is related...

We present a fully deterministic approach to a probabilistic interpretation of inverse problems in which unknown quantities are represented by random fields or processes, described by a non-Gaussian prior distribution. The description of the introduced random fields is given in a "white noise" framework, which enables us to solve the stochastic for...

The mathematical formulation and numerical simulation of an elastic-plastic material with uncertain parameters in the small strain case is considered. Traditional computational approaches to this problem usually use some form of perturbation or Monte Carlo technique. This is contrasted here with more recent methods based on stochastic Galerkin appr...

We analyse the stochastic finite element method for a class of mixed variational inequalities of the second kind, which arises in elastoplastic problems. The quasi-static von Mises elastoplastic rate-independent evolution problem with linear isotropic hardening is considered with the emphasis on the presence of uncertainty in the description of mat...

In this paper we consider the mixed variational formulation of the quasi-static stochastic plasticity with combined isotropic and kinematic hardening. By applying standard results in convex analysis we show that criteria for the existence, uniqueness, and convergence can be easily derived. In addition, we demonstrate the mathematical similarity wit...

We present a new, fully deterministic method to compute the updates for parameter estimates of quasi-static plasticity with combined kinematic and isotropic hardening from noisy measurements. The materials describing the elastic (reversible) and/or inelastic (irreversible) behaviour have an uncertain structure which further influences the uncertain...

Introducing input parameters as random fields and processes and applying a stochastic Galerkin approximation, we obtain opportunity to develop the stochastic radial return mapping algorithm for the case of hardening plasticity in domain of small deformations. Further on, we consider more complicated situations connected with the problem of large de...

We discuss inelastic media under uncertainty modelled by probabilistic methods. As a prototype inelastic material we consider perfect plasticity. We propose a mathematical formulation as a stochastic variational inequality. The new element vis à vis a stochastic elastic medium is the so-called return map at each Gauss-point. We concentrate on a sto...

In this paper we will consider inelastic material described with uncertain parameters like bulk and shear modulus as well as yield stress, described as lognormal random fields. Uncertainty also can appear on the right hand side of the equilibrium equation. These uncertainties define stochastic inelastic problem, computationally treated by Karhunen-...

Heterogeneities at the micro-structural level are usually subjected to a number of uncertainties. There it is assumed that the heterogeneous material behaves according to an elasto-plastic model, but with uncertain parameters, which are modeled as random fields like Young's modulus, yield stress etc. Actually, we try to model the simplest variant w...

We present a fully deterministic method to compute sequential updates for stochastic state estimates of dynamic models from noisy measurements. It does not need any assumptions about the type of distribution for either data or measurement — in particular it does not have to assume any of them as Gaussian. It is based on a polynomial chaos expansion...