ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2007.05384
  4. Cited By
Deep neural network approximation for high-dimensional elliptic PDEs
  with boundary conditions

Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions

10 July 2020
Philipp Grohs
L. Herrmann
ArXivPDFHTML

Papers citing "Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions"

20 / 20 papers shown
Title
Sampling Complexity of Deep Approximation Spaces
Sampling Complexity of Deep Approximation Spaces
Ahmed Abdeljawad
Philipp Grohs
26
1
0
20 Dec 2023
Residual Multi-Fidelity Neural Network Computing
Residual Multi-Fidelity Neural Network Computing
Owen Davis
Mohammad Motamed
Raúl Tempone
37
1
0
05 Oct 2023
A numerical approach for the fractional Laplacian via deep neural
  networks
A numerical approach for the fractional Laplacian via deep neural networks
Nicolás Valenzuela
24
3
0
30 Aug 2023
Theoretical guarantees for neural control variates in MCMC
Theoretical guarantees for neural control variates in MCMC
Denis Belomestny
Artur Goldman
A. Naumov
S. Samsonov
BDL
DRL
11
6
0
03 Apr 2023
Multilevel CNNs for Parametric PDEs
Multilevel CNNs for Parametric PDEs
Cosmas Heiß
Ingo Gühring
Martin Eigel
AI4CE
17
8
0
01 Apr 2023
Neural Network Approximations of PDEs Beyond Linearity: A
  Representational Perspective
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective
Tanya Marwah
Zachary Chase Lipton
Jianfeng Lu
Andrej Risteski
44
10
0
21 Oct 2022
From Monte Carlo to neural networks approximations of boundary value
  problems
From Monte Carlo to neural networks approximations of boundary value problems
L. Beznea
Iulian Cîmpean
Oana Lupascu-Stamate
Ionel Popescu
A. Zarnescu
14
1
0
03 Sep 2022
Robust SDE-Based Variational Formulations for Solving Linear PDEs via
  Deep Learning
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning
Lorenz Richter
Julius Berner
19
19
0
21 Jun 2022
Compressive Fourier collocation methods for high-dimensional diffusion
  equations with periodic boundary conditions
Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions
Weiqi Wang
Simone Brugiapaglia
17
2
0
02 Jun 2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic
  Equations via Gradient Descent
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent
Yiping Lu
Jose H. Blanchet
Lexing Ying
35
7
0
15 May 2022
FinNet: Solving Time-Independent Differential Equations with Finite
  Difference Neural Network
FinNet: Solving Time-Independent Differential Equations with Finite Difference Neural Network
Son N. T. Tu
Thu Nguyen
AI4CE
11
0
0
18 Feb 2022
Exponential Convergence of Deep Operator Networks for Elliptic Partial
  Differential Equations
Exponential Convergence of Deep Operator Networks for Elliptic Partial Differential Equations
C. Marcati
Christoph Schwab
14
38
0
15 Dec 2021
Interpolating between BSDEs and PINNs: deep learning for elliptic and
  parabolic boundary value problems
Interpolating between BSDEs and PINNs: deep learning for elliptic and parabolic boundary value problems
Nikolas Nusken
Lorenz Richter
PINN
DiffM
31
27
0
07 Dec 2021
Approximation properties of Residual Neural Networks for Kolmogorov PDEs
Approximation properties of Residual Neural Networks for Kolmogorov PDEs
Jonas Baggenstos
Diyora Salimova
15
3
0
30 Oct 2021
On the Representation of Solutions to Elliptic PDEs in Barron Spaces
On the Representation of Solutions to Elliptic PDEs in Barron Spaces
Ziang Chen
Jianfeng Lu
Yulong Lu
25
26
0
14 Jun 2021
Deep neural network approximation for high-dimensional parabolic
  Hamilton-Jacobi-Bellman equations
Deep neural network approximation for high-dimensional parabolic Hamilton-Jacobi-Bellman equations
Philipp Grohs
L. Herrmann
9
6
0
09 Mar 2021
Parametric Complexity Bounds for Approximating PDEs with Neural Networks
Parametric Complexity Bounds for Approximating PDEs with Neural Networks
Tanya Marwah
Zachary Chase Lipton
Andrej Risteski
15
19
0
03 Mar 2021
Quantitative approximation results for complex-valued neural networks
Quantitative approximation results for complex-valued neural networks
A. Caragea
D. Lee
J. Maly
G. Pfander
F. Voigtlaender
11
5
0
25 Feb 2021
Approximations with deep neural networks in Sobolev time-space
Approximations with deep neural networks in Sobolev time-space
Ahmed Abdeljawad
Philipp Grohs
14
12
0
23 Dec 2020
An overview on deep learning-based approximation methods for partial
  differential equations
An overview on deep learning-based approximation methods for partial differential equations
C. Beck
Martin Hutzenthaler
Arnulf Jentzen
Benno Kuckuck
30
146
0
22 Dec 2020
1