Improved architectures and training algorithms for deep operator
networks

Improved architectures and training algorithms for deep operator networks

4 October 2021

Papers citing "Improved architectures and training algorithms for deep operator networks"

19 / 19 papers shown

Title
Deep Optimal Sensor Placement for Black Box Stochastic Simulations Paula Cordero-Encinar Tobias Schröder P. Yatsyshin Andrew Duncan 45 0 0 15 Oct 2024
Deep Learning Alternatives of the Kolmogorov Superposition Theorem Leonardo Ferreira Guilhoto P. Perdikaris 38 7 0 02 Oct 2024
Physics-Informed Geometry-Aware Neural Operator Weiheng Zhong Hadi Meidani AI4CE 33 4 0 02 Aug 2024
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations N. McGreivy Ammar Hakim AI4CE 29 42 0 09 Jul 2024
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains Yusuke Yamazaki Ali Harandi Mayu Muramatsu A. Viardin Markus Apel T. Brepols Stefanie Reese Shahed Rezaei AI4CE 30 12 0 21 May 2024
Neural Parameter Regression for Explicit Representations of PDE Solution Operators Konrad Mundinger Max Zimmer S. Pokutta 42 0 0 19 Mar 2024
Variational Autoencoding Neural Operators Jacob H. Seidman Georgios Kissas George J. Pappas P. Perdikaris DRL AI4CE 25 7 0 20 Feb 2023
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation Rui Zhang Qi Meng Rongchan Zhu Yue Wang Wenlei Shi Shihua Zhang Zhi-Ming Ma Tie-Yan Liu DiffM AI4CE 44 4 0 10 Feb 2023
Deep learning for full-field ultrasonic characterization Yang Xu Fatemeh Pourahmadian Jian Song Congli Wang AI4CE 29 4 0 06 Jan 2023
NOMAD: Nonlinear Manifold Decoders for Operator Learning Jacob H. Seidman Georgios Kissas P. Perdikaris George J. Pappas AI4CE 23 68 0 07 Jun 2022
Transformer for Partial Differential Equations' Operator Learning Zijie Li Kazem Meidani A. Farimani 42 140 0 26 May 2022
Physics-informed neural networks for PDE-constrained optimization and control Jostein Barry-Straume A. Sarshar Andrey A. Popov Adrian Sandu PINN AI4CE 27 14 0 06 May 2022
Respecting causality is all you need for training physics-informed neural networks Sifan Wang Shyam Sankaran P. Perdikaris PINN CML AI4CE 30 199 0 14 Mar 2022
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors Yibo Yang Georgios Kissas P. Perdikaris BDL UQCV 20 40 0 06 Mar 2022
Learning Operators with Coupled Attention Georgios Kissas Jacob H. Seidman Leonardo Ferreira Guilhoto V. Preciado George J. Pappas P. Perdikaris 24 109 0 04 Jan 2022
Fast PDE-constrained optimization via self-supervised operator learning Sifan Wang Mohamed Aziz Bhouri P. Perdikaris 40 28 0 25 Oct 2021
Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks Annan Yu Chloe Becquey Diana Halikias Matthew Esmaili Mallory Alex Townsend 57 8 0 23 Sep 2021
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks Sifan Wang Hanwen Wang P. Perdikaris 131 438 0 18 Dec 2020
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 2,282 0 18 Oct 2020