Deep Operator Network Approximation Rates for Lipschitz Operators

19 July 2023

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

11 / 11 papers shown

Title
Nonlocality and Nonlinearity Implies Universality in Operator Learning S. Lanthaler Zong-Yi Li Andrew M. Stuart 59 17 0 26 Apr 2023
Operator learning with PCA-Net: upper and lower complexity bounds S. Lanthaler 60 26 0 28 Mar 2023
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning Thomas O'Leary-Roseberry Peng Chen Umberto Villa Omar Ghattas AI4CE 61 41 0 21 Jun 2022
Transformer for Partial Differential Equations' Operator Learning Zijie Li Kazem Meidani A. Farimani 109 170 0 26 May 2022
Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems Amanda A. Howard M. Perego G. Karniadakis P. Stinis AI4CE 74 56 0 19 Apr 2022
De Rham compatible Deep Neural Network FEM M. Longo J. Opschoor Nico Disch Christoph Schwab Jakob Zech 62 8 0 14 Jan 2022
Physics-informed neural networks (PINNs) for fluid mechanics: A review Shengze Cai Zhiping Mao Zhicheng Wang Minglang Yin George Karniadakis PINN AI4CE 82 1,198 0 20 May 2021
Quantitative approximation results for complex-valued neural networks A. Caragea D. Lee J. Maly G. Pfander F. Voigtlaender 42 6 0 25 Feb 2021
Elementary superexpressive activations Dmitry Yarotsky 64 36 0 22 Feb 2021
A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale Michael G. Kapteyn Jacob V. R. Pretorius Karen E. Willcox 59 228 0 10 Dec 2020
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 248 2,153 0 08 Oct 2019