Deep Operator Network Approximation Rates for Lipschitz Operators

19 July 2023

Papers citing "Deep Operator Network Approximation Rates for Lipschitz Operators"

10 / 10 papers shown

Title
New universal operator approximation theorem for encoder-decoder architectures (Preprint) Janek Gödeke Pascal Fernsel 35 0 0 31 Mar 2025
DiGRAF: Diffeomorphic Graph-Adaptive Activation Function Krishna Sri Ipsit Mantri Xinzhi Wang Carola-Bibiane Schönlieb Bruno Ribeiro Beatrice Bevilacqua Moshe Eliasof GNN 49 1 0 02 Jul 2024
Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective Samuel Lanthaler 39 3 0 26 Jun 2024
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 42 4 0 04 Apr 2024
Operator Learning: Algorithms and Analysis Nikola B. Kovachki S. Lanthaler Andrew M. Stuart 43 22 0 24 Feb 2024
Operator learning without the adjoint Nicolas Boullé Diana Halikias Samuel E. Otto Alex Townsend 26 4 0 31 Jan 2024
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries Zong-Yi Li Daniel Zhengyu Huang Burigede Liu Anima Anandkumar AI4CE 119 249 0 11 Jul 2022
Neural and spectral operator surrogates: unified construction and expression rate bounds L. Herrmann Christoph Schwab Jakob Zech 45 9 0 11 Jul 2022
Neural Network Architecture Beyond Width and Depth Zuowei Shen Haizhao Yang Shijun Zhang 3DV MDE 31 13 0 19 May 2022
A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale Michael G. Kapteyn Jacob V. R. Pretorius Karen E. Willcox 34 214 0 10 Dec 2020