Reliable extrapolation of deep neural operators informed by physics or sparse observations

13 December 2022

Papers citing "Reliable extrapolation of deep neural operators informed by physics or sparse observations"

8 / 8 papers shown

Title
State-space models are accurate and efficient neural operators for dynamical systems Zheyuan Hu Nazanin Ahmadi Daryakenari Qianli Shen Kenji Kawaguchi George Karniadakis Mamba AI4CE 64 10 0 28 Jan 2025
Branched Latent Neural Maps M. Salvador Alison Lesley Marsden 25 4 0 04 Aug 2023
In-Context Operator Learning with Data Prompts for Differential Equation Problems Liu Yang Siting Liu Tingwei Meng Stanley J. Osher 14 53 0 17 Apr 2023
Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks K. Michałowska S. Goswami George Karniadakis S. Riemer-Sørensen AI4CE 15 15 0 03 Mar 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 20 19 0 02 Feb 2023
Neural and spectral operator surrogates: unified construction and expression rate bounds L. Herrmann Christoph Schwab Jakob Zech 33 9 0 11 Jul 2022
Generic bounds on the approximation error for physics-informed (and) operator learning Tim De Ryck Siddhartha Mishra PINN 56 58 0 23 May 2022
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 203 2,254 0 18 Oct 2020