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Deep-OSG: Deep Learning of Operators in Semigroup

Deep-OSG: Deep Learning of Operators in Semigroup

7 February 2023
Junfeng Chen
Kailiang Wu
    AI4TS
ArXivPDFHTML

Papers citing "Deep-OSG: Deep Learning of Operators in Semigroup"

14 / 14 papers shown
Title
Flow Map Learning for Unknown Dynamical Systems: Overview,
  Implementation, and Benchmarks
Flow Map Learning for Unknown Dynamical Systems: Overview, Implementation, and Benchmarks
V. Churchill
D. Xiu
AI4CE
52
10
0
20 Jul 2023
Deep Learning of Chaotic Systems from Partially-Observed Data
Deep Learning of Chaotic Systems from Partially-Observed Data
V. Churchill
D. Xiu
70
12
0
12 May 2022
Neural Operator: Learning Maps Between Function Spaces
Neural Operator: Learning Maps Between Function Spaces
Nikola B. Kovachki
Zong-Yi Li
Burigede Liu
Kamyar Azizzadenesheli
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
AI4CE
99
446
0
19 Aug 2021
Any equation is a forest: Symbolic genetic algorithm for discovering
  open-form partial differential equations (SGA-PDE)
Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)
Yuntian Chen
Yingtao Luo
Qiang Liu
Hao Xu
Dongxiao Zhang
AI4CE
55
57
0
09 Jun 2021
Fourier Neural Operator for Parametric Partial Differential Equations
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
468
2,384
0
18 Oct 2020
DLGA-PDE: Discovery of PDEs with incomplete candidate library via
  combination of deep learning and genetic algorithm
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
Hao Xu
Haibin Chang
Dongxiao Zhang
AI4CE
47
89
0
21 Jan 2020
Discovery of Dynamics Using Linear Multistep Methods
Discovery of Dynamics Using Linear Multistep Methods
Rachael Keller
Q. Du
45
36
0
29 Dec 2019
DeepONet: Learning nonlinear operators for identifying differential
  equations based on the universal approximation theorem of operators
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
Lu Lu
Pengzhan Jin
George Karniadakis
210
2,108
0
08 Oct 2019
Data-driven discovery of free-form governing differential equations
Data-driven discovery of free-form governing differential equations
Steven Atkinson
W. Subber
Liping Wang
Genghis Khan
Philippe Hawi
R. Ghanem
44
43
0
27 Sep 2019
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations
  for Modeling Time-Dependent Data
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data
Yifan Sun
Linan Zhang
Hayden Schaeffer
AI4TS
52
91
0
08 Aug 2019
Structure-preserving Method for Reconstructing Unknown Hamiltonian
  Systems from Trajectory Data
Structure-preserving Method for Reconstructing Unknown Hamiltonian Systems from Trajectory Data
Kailiang Wu
Tong Qin
D. Xiu
43
31
0
24 May 2019
Data Driven Governing Equations Approximation Using Deep Neural Networks
Data Driven Governing Equations Approximation Using Deep Neural Networks
Tong Qin
Kailiang Wu
D. Xiu
PINN
62
272
0
13 Nov 2018
PDE-Net: Learning PDEs from Data
PDE-Net: Learning PDEs from Data
Zichao Long
Yiping Lu
Xianzhong Ma
Bin Dong
DiffM
AI4CE
38
754
0
26 Oct 2017
Cyclical Learning Rates for Training Neural Networks
Cyclical Learning Rates for Training Neural Networks
L. Smith
ODL
181
2,517
0
03 Jun 2015
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