DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

8 October 2019

Papers citing "DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators"

7 / 207 papers shown

Title
Metric Entropy Limits on Recurrent Neural Network Learning of Linear Dynamical Systems Clemens Hutter R. Gül Helmut Bölcskei 11 9 0 06 May 2021
Two-layer neural networks with values in a Banach space Yury Korolev 13 23 0 05 May 2021
A Deep Learning approach to Reduced Order Modelling of Parameter Dependent Partial Differential Equations N. R. Franco Andrea Manzoni P. Zunino 16 44 0 10 Mar 2021
Modern Koopman Theory for Dynamical Systems Steven L. Brunton M. Budišić E. Kaiser J. Nathan Kutz AI4CE 11 387 0 24 Feb 2021
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems Pengzhan Jin Zhen Zhang Aiqing Zhu Yifa Tang George Karniadakis 9 21 0 11 Jan 2020
Symplectic Recurrent Neural Networks Zhengdao Chen Jianyu Zhang Martín Arjovsky Léon Bottou 139 219 0 29 Sep 2019
A Multi-level procedure for enhancing accuracy of machine learning algorithms K. Lye Siddhartha Mishra Roberto Molinaro 11 31 0 20 Sep 2019