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Computational Mechanics
Published in: Computational Mechanics 6/2023

25-03-2023 | Original Paper

Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets

Authors: Subhayan De, Matthew Reynolds, Malik Hassanaly, Ryan N. King, Alireza Doostan

Published in: Computational Mechanics | Issue 6/2023

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Abstract

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. For these systems, often computationally inexpensive, but in general inaccurate models, known as the low-fidelity models, are available. In this paper, we propose a bi-fidelity modeling approach for complex physical systems, where we model the discrepancy between the true system’s response and a low-fidelity response in the presence of a small training dataset from the true system’s response using a deep operator network, a neural network architecture suitable for approximating nonlinear operators. We apply the approach to systems that have parametric uncertainty and are partially unknown. Three numerical examples are used to show the efficacy of the proposed approach to model uncertain and partially unknown physical systems.
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Metadata
Title
Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets
Authors
Subhayan De
Matthew Reynolds
Malik Hassanaly
Ryan N. King
Alireza Doostan
Publication date
25-03-2023
Publisher
Springer Berlin Heidelberg
Published in
Computational Mechanics / Issue 6/2023
Print ISSN: 0178-7675
Electronic ISSN: 1432-0924
DOI
https://doi.org/10.1007/s00466-023-02272-4

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