Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems

19 April 2022

Papers citing "Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems"

9 / 9 papers shown

Title
Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operators Hanxun Jin Enrui Zhang Boyu Zhang Sridhar Krishnaswamy George Karniadakis Horacio D. Espinosa AI4CE 32 4 0 23 Nov 2023
Branched Latent Neural Maps M. Salvador Alison Lesley Marsden 35 4 0 04 Aug 2023
A Bi-fidelity DeepONet Approach for Modeling Uncertain and Degrading Hysteretic Systems Subhayan De P. Brewick 31 0 0 25 Apr 2023
A DeepONet multi-fidelity approach for residual learning in reduced order modeling N. Demo M. Tezzele G. Rozza 30 19 0 24 Feb 2023
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils K. Shukla Vivek Oommen Ahmad Peyvan Michael Penwarden L. Bravo A. Ghoshal Robert M. Kirby George Karniadakis 33 19 0 02 Feb 2023
Multi-fidelity wavelet neural operator with application to uncertainty quantification A. Thakur Tapas Tripura S. Chakraborty 30 12 0 11 Aug 2022
Multi-fidelity surrogate modeling using long short-term memory networks Paolo Conti Mengwu Guo Andrea Manzoni J. Hesthaven AI4CE 33 48 0 05 Aug 2022
Improved architectures and training algorithms for deep operator networks Sizhuang He Hanwen Wang P. Perdikaris AI4CE 52 105 0 04 Oct 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 226 2,287 0 18 Oct 2020