Bi-Fidelity Modeling of Uncertain and Partially Unknown Systems Using DeepONets

Subhayan De, Matthew Reynolds, Malik Hassanaly, Ryan King, Alireza Doostan

Research output: Contribution to journalArticlepeer-review

3 Scopus Citations


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.

Original languageAmerican English
Pages (from-to)1251-1267
Number of pages17
JournalComputational Mechanics
Issue number6
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

NREL Publication Number

  • NREL/JA-2C00-82567


  • Bi-fidelity method
  • Deep operator network
  • Neural network
  • Uncertain system
  • Uncertainty quantification


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