1 code implementation • 28 Dec 2023 • Malik Hassanaly, Peter J. Weddle, Ryan N. King, Subhayan De, Alireza Doostan, Corey R. Randall, Eric J. Dufek, Andrew M. Colclasure, Kandler Smith
To reduce the computational cost of Bayesian calibration, numerical solvers for physics-based models can be replaced with faster surrogates.
1 code implementation • 28 Dec 2023 • Malik Hassanaly, Peter J. Weddle, Ryan N. King, Subhayan De, Alireza Doostan, Corey R. Randall, Eric J. Dufek, Andrew M. Colclasure, Kandler Smith
The techniques used to develop a PINN surrogate of the SPM are extended in Part II for the PINN surrogate for the P2D battery model, and explore the Bayesian calibration capabilities of both surrogates.
1 code implementation • 25 May 2023 • Nuojin Cheng, Osman Asif Malik, Subhayan De, Stephen Becker, Alireza Doostan
An effective algorithm is proposed to maximize the variational lower bound of the HF log-likelihood in the presence of limited HF data, resulting in the synthesis of HF realizations with a reduced computational cost.
no code implementations • 25 Apr 2023 • Subhayan De, Patrick T. Brewick
Nonlinear systems, such as with degrading hysteretic behavior, are often encountered in engineering applications.
no code implementations • 3 Apr 2022 • Subhayan De, Matthew Reynolds, Malik Hassanaly, Ryan N. King, Alireza Doostan
Recent advances in modeling large-scale complex physical systems have shifted research focuses towards data-driven techniques.
no code implementations • 27 May 2021 • Subhayan De, Alireza Doostan
These bi-fidelity strategies are generalizations of transfer learning of neural networks that uses the parameters learned from a large low-fidelity dataset to efficiently train networks for a small high-fidelity dataset.
no code implementations • 30 Mar 2021 • Subhayan De, Bhuiyan Shameem Mahmood Ebna Hai, Alireza Doostan, Markus Bause
The physics model used in this study comprises of a monolithically coupled system of acoustic and elastic wave equations, known as the wave propagation in fluid-solid and their interface (WpFSI) problem.
no code implementations • 11 Aug 2020 • Subhayan De
A standard nonlinear solver for them with sampling-based methods for uncertainty quantification incurs significant computational cost for estimating the statistics of the response.
no code implementations • 11 Feb 2020 • Subhayan De, Jolene Britton, Matthew Reynolds, Ryan Skinner, Kenneth Jansen, Alireza Doostan
In the former approach, a neural network model mapping the inputs to the outputs of interest is trained based on the low-fidelity data.
1 code implementation • 11 Feb 2019 • Subhayan De, Jerrad Hampton, Kurt Maute, Alireza Doostan
To tackle this difficulty, we here propose an optimization approach that generates a stochastic approximation of the objective, constraints, and their gradients via a small number of adjoint (and/or forward) solves, per optimization iteration.
Optimization and Control Numerical Analysis