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Data-driven discovery of Green's functions with human-understandable deep learning

Data-driven discovery of Green's functions with human-understandable deep learning

Nicolas Boullé
Christopher Earls
Alex Townsend
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Papers citing "Data-driven discovery of Green's functions with human-understandable deep learning"

7 / 7 papers shown
Title
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains
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
39
12
0
21 May 2024
Approximation of Solution Operators for High-dimensional PDEs
Approximation of Solution Operators for High-dimensional PDEs
Nathan Gaby
Xiaojing Ye
30
0
0
18 Jan 2024
Operator learning for hyperbolic partial differential equations
Operator learning for hyperbolic partial differential equations
Christopher Wang
Alex Townsend
42
2
0
29 Dec 2023
Elliptic PDE learning is provably data-efficient
Elliptic PDE learning is provably data-efficient
N. Boullé
Diana Halikias
Alex Townsend
28
18
0
24 Feb 2023
Learning Green's functions associated with time-dependent partial differential equations
Learning Green's functions associated with time-dependent partial differential equations
N. Boullé
Seick Kim
Tianyi Shi
Alex Townsend
AI4CE
29
25
0
27 Apr 2022
Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces
Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces
George Stepaniants
49
15
0
26 Aug 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
262
2,309
0
18 Oct 2020