Subhayan De

Subhayan De
University of Colorado Boulder | CUB · Department of Aerospace Engineering Sciences (AES)

Doctor of Philosophy

About

25
Publications
3,438
Reads
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151
Citations
Citations since 2016
24 Research Items
151 Citations
20162017201820192020202120220102030405060
20162017201820192020202120220102030405060
20162017201820192020202120220102030405060
20162017201820192020202120220102030405060
Additional affiliations
June 2018 - present
University of Colorado Boulder
Position
  • PostDoc Position
Education
August 2013 - May 2016
University of Southern California
Field of study
  • Electrical Engineering
August 2013 - May 2018
University of Southern California
Field of study
  • Civil Engineering
August 2011 - July 2013
Indian Institute of Science
Field of study
  • Structural Engineering

Publications

Publications (25)
Article
Li-ion batteries (LIB) are a promising solution to enable the storage of intermittent energy sources due to their high energy density. However, LIBs are known to significantly degrade after about 1000 charge-discharge cycles. LIBs degrade following different degradation modes and at a rate that depends on the operating conditions (e.g., external te...
Article
Full-text available
This paper studies design problems where the performance is dominated by the dynamic evolution of interfaces due to chemical processes. Considering the representative example of a solid rocket motor, the shape of the interface between the solid fuel and the gas inside the combustion chamber at the beginning of the burn process and the reference bur...
Preprint
Full-text available
Recent advances in modeling large-scale complex physical systems have shifted research focuses towards data-driven techniques. However, generating datasets by simulating complex systems can require significant computational resources. Similarly, acquiring experimental datasets can prove difficult as well. For these systems, often computationally in...
Article
With the capability of accurately representing a functional relationship between the inputs of a physical system's model and output quantities of interest, neural networks have become popular for surrogate modeling in scientific applications. However, as these networks are over-parameterized, their training often requires a large amount of data. To...
Article
Full-text available
This paper addresses the computational challenges in reliability-based topology optimization (RBTO) of structures associated with the estimation of statistics of the objective and constraints using standard sampling methods. The aim is to overcome the accuracy issues of traditional methods that rely on approximating the limit-state function. Herein...
Preprint
Full-text available
This paper considers the design of structures made of engineered materials, accounting for uncertainty in material properties. We present a topology optimization approach that optimizes the structural shape and topology at the macroscale assuming design-independent uncertain microstructures. The structural geometry at the macroscale is described by...
Article
Structural health monitoring (SHM) systems use non-destructive testing principle for damage identification. As part of SHM, the propagation of ultrasonic guided waves (UGWs) is tracked and analyzed for the changes in the associated wave pattern. These changes help identify the location of a structural damage, if any. We advance existing research by...
Preprint
Full-text available
With the capability of accurately representing a functional relationship between the inputs of a physical system's model and output quantities of interest, neural networks have become popular for surrogate modeling in scientific applications. However, as these networks are over-parameterized, their training often requires a large amount of data. To...
Preprint
Full-text available
Structural health monitoring (SHM) systems use non-destructive testing principle for damage identification. As part of SHM, the propagation of ultrasonic guided waves (UGWs) is tracked and analyzed for the changes in the associated wave pattern. These changes help identify the location of a structural damage, if any. We advance existing research by...
Preprint
Full-text available
This paper addresses the computational challenges in reliability-based topology optimization (RBTO) of structures associated with the estimation of statistics of the objective and constraints using standard sampling methods, and overcomes the accuracy issues of traditional methods that rely on approximating the limit state function. Herein, we pres...
Preprint
This paper addresses the computational challenges in reliability-based topology optimization (RBTO) of structures associated with the estimation of statistics of the objective and constraints using standard sampling methods, and overcomes the accuracy issues of traditional methods that rely on approximating the limit state function. Herein, we pres...
Article
Full-text available
Topology optimization under uncertainty (TOuU) often defines objectives and constraints by statistical moments of geometric and physical quantities of interest. Most traditional TOuU methods use gradient-based optimization algorithms and rely on accurate estimates of the statistical moments and their gradients, e.g., via adjoint calculations. When...
Article
Full-text available
The presence of uncertainty in material properties and geometry of a structure is ubiquitous. The design of robust engineering structures, therefore, needs to incorporate uncertainty in the optimization process. Stochastic gradient descent (SGD) method can alleviate the cost of optimization under uncertainty, which includes statistical moments of q...
Preprint
Full-text available
(https://arxiv.org/abs/2008.04598) -- Models are often given in terms of differential equations to represent physical systems. In the presence of uncertainty, accurate prediction of the behavior of these systems using the models requires understanding the effect of uncertainty in the response. In uncertainty quantification, statistics such as mean...
Preprint
Full-text available
Due to their high degree of expressiveness, neural networks have recently been used as surrogate models for mapping inputs of an engineering system to outputs of interest. Once trained, neural networks are computationally inexpensive to evaluate and remove the need for repeated evaluations of computationally expensive models in uncertainty quantifi...
Preprint
Full-text available
The presence of uncertainty in material properties and geometry of a structure is ubiquitous. The design of robust engineering structures, therefore, needs to incorporate uncertainty in the optimization process. Stochastic gradient descent (SGD) method can alleviate the cost of optimization under uncertainty, which includes statistical moments of q...
Article
Identifying useful mathematical models of physical systems is an essential part of computational modeling and simulation. Once appropriate models are identified, they can be used for applications such as response prediction, structural control, monitoring structural integrity, lifetime prognosis, etc. The number of models and model classes availabl...
Preprint
Full-text available
Topology optimization under uncertainty (TOuU) often defines objectives and constraints by statistical moments of geometric and physical quantities of interest. Most traditional TOuU methods use gradient-based optimization algorithms and rely on accurate estimates of the statistical moments and their gradients, e.g, via adjoint calculations. When t...
Article
Models, typically given by systems of mathematical equations, are built to help represent, understand and further characterize physical phenomena. The choice of a model for a particular phenomenon is made based on user judgment, evidence from measurement data, and/or the ease of its use. Generally, many linear and nonlinear models are available to...
Article
Models are used to represent and characterize physical phenomena. When there are many plausible models for a particular phenomenon, the modeler can exploit the computational tool called model falsification to systematically eliminate models that do not reasonably fit measured data. Model falsification typically compares measurements and their predi...
Article
Structures today may be equipped with passive structural control devices to achieve some performance criteria. The optimal design of these passive control devices, whether via a formal optimization algorithm or a response surface parameter study, requires multiple solutions of the dynamic response of that structure, incurring a significant computat...
Conference Paper
In Bayesian model selection, suitable mathematical models are selected among a set of possible models using Bayes’ theorem. To simplify the analysis, linear structural models are often used, though they are not always adequate to accurately compute the structural response. Nonlinear models, which may be more accurate, significantly increase the req...

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Projects

Projects (5)
Project
This project addresses the efficient training of neural networks for uncertainty quantification in the presence of a multi-fidelity dataset.
Project
Application of Stochastic Gradient Descent methods to TOuU