DGM: A deep learning algorithm for solving partial differential equations

24 August 2017

Papers citing "DGM: A deep learning algorithm for solving partial differential equations"

50 / 315 papers shown

Title
HOPE: High-order Polynomial Expansion of Black-box Neural Networks Tingxiong Xiao Weihang Zhang Yuxiao Cheng J. Suo 39 2 0 17 Jul 2023
Neural Stream Functions Skylar W. Wurster Hanqi Guo Tom Peterka Han-Wei Shen 26 0 0 16 Jul 2023
Stochastic Delay Differential Games: Financial Modeling and Machine Learning Algorithms R. Balkin Héctor D. Ceniceros Ruimeng Hu 26 2 0 12 Jul 2023
Temporal Difference Learning for High-Dimensional PIDEs with Jumps Liwei Lu Hailong Guo Xueqing Yang Yi Zhu AI4CE 28 6 0 06 Jul 2023
Accelerated primal-dual methods with enlarged step sizes and operator learning for nonsmooth optimal control problems Yongcun Song Xiaoming Yuan Hangrui Yue AI4CE 21 2 0 01 Jul 2023
Addressing Discontinuous Root-Finding for Subsequent Differentiability in Machine Learning, Inverse Problems, and Control Dan Johnson Ronald Fedkiw AI4CE 36 2 0 21 Jun 2023
A Finite Expression Method for Solving High-Dimensional Committor Problems Zezheng Song M. Cameron Haizhao Yang 23 6 0 21 Jun 2023
ParticleWNN: a Novel Neural Networks Framework for Solving Partial Differential Equations Yaohua Zang Gang Bao 37 4 0 21 May 2023
Global Convergence of Deep Galerkin and PINNs Methods for Solving Partial Differential Equations Deqing Jiang Justin A. Sirignano Samuel N. Cohen 24 6 0 10 May 2023
A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks Marc Finzi Andres Potapczynski M. Choptuik A. Wilson 26 12 0 28 Apr 2023
Pseudo-Hamiltonian neural networks for learning partial differential equations Sølve Eidnes K. Lye 31 10 0 27 Apr 2023
A Survey on Solving and Discovering Differential Equations Using Deep Neural Networks Hyeonjung Jung Jung Jayant Gupta B. Jayaprakash Matthew J. Eagon Harish Selvam Carl Molnar W. Northrop Shashi Shekhar AI4CE 45 5 0 26 Apr 2023
In-Context Operator Learning with Data Prompts for Differential Equation Problems Liu Yang Siting Liu Tingwei Meng Stanley J. Osher 40 60 0 17 Apr 2023
EPINN-NSE: Enhanced Physics-Informed Neural Networks for Solving Navier-Stokes Equations Ayoub Farkane Mounir Ghogho M. Oudani M. Boutayeb PINN 28 5 0 07 Apr 2023
$Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion$ Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion Xiongbin Yan Zhi-Qin John Xu Zheng Ma 32 2 0 03 Apr 2023
Multilevel CNNs for Parametric PDEs Cosmas Heiß Ingo Gühring Martin Eigel AI4CE 30 8 0 01 Apr 2023
GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs Yuling Jiao Dingwei Li Xiliang Lu J. Yang Cheng Yuan 39 9 0 28 Mar 2023
GNN-based physics solver for time-independent PDEs R. J. Gladstone H. Rahmani V. Suryakumar Hadi Meidani M. DÉlia A. Zareei AI4CE 27 15 0 28 Mar 2023
Error Analysis of Physics-Informed Neural Networks for Approximating Dynamic PDEs of Second Order in Time Y. Qian Yongchao Zhang Yuanfei Huang S. Dong PINN 26 1 0 22 Mar 2023
Deep Learning for Mean Field Optimal Transport Sebastian Baudelet Brieuc Frénais Mathieu Laurière Amal Machtalay Yuchen Zhu OT 28 2 0 28 Feb 2023
Achieving High Accuracy with PINNs via Energy Natural Gradients Johannes Müller Marius Zeinhofer 18 5 0 25 Feb 2023
Physics informed WNO N. N. Tapas Tripura S. Chakraborty 33 28 0 12 Feb 2023
A Policy Gradient Framework for Stochastic Optimal Control Problems with Global Convergence Guarantee Mo Zhou Jian-Xiong Lu 38 7 0 11 Feb 2023
A conceptual model for leaving the data-centric approach in machine learning S. Scher Bernhard C. Geiger Simone Kopeinik A. Trugler Dominik Kowald 39 0 0 07 Feb 2023
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations Arnulf Jentzen Adrian Riekert Philippe von Wurstemberger 34 1 0 07 Feb 2023
Failure-informed adaptive sampling for PINNs, Part II: combining with re-sampling and subset simulation Zhi-Hao Gao Tao Tang Liang Yan Tao Zhou 37 18 0 03 Feb 2023
Learning PDE Solution Operator for Continuous Modeling of Time-Series Yesom Park Jaemoo Choi Changyeon Yoon Changhoon Song Myung-joo Kang AI4TS AI4CE 27 3 0 02 Feb 2023
Experimental observation on a low-rank tensor model for eigenvalue problems Jun Hu Pengzhan Jin 19 2 0 01 Feb 2023
Bridging Physics-Informed Neural Networks with Reinforcement Learning: Hamilton-Jacobi-Bellman Proximal Policy Optimization (HJBPPO) Amartya Mukherjee Jun Liu 20 11 0 01 Feb 2023
Computability of Optimizers Yunseok Lee Holger Boche Gitta Kutyniok 35 16 0 15 Jan 2023
Learning Partial Differential Equations by Spectral Approximates of General Sobolev Spaces Juan Esteban Suarez Cardona Phil-Alexander Hofmann Michael Hecht 32 3 0 12 Jan 2023
New Designed Loss Functions to Solve Ordinary Differential Equations with Artificial Neural Network Xiao Xiong 21 0 0 29 Dec 2022
Enhancing Neural Network Differential Equation Solvers Matthew J. H. Wright 21 1 0 28 Dec 2022
Physics-informed Neural Networks with Periodic Activation Functions for Solute Transport in Heterogeneous Porous Media Salah A. Faroughi Ramin Soltanmohammad Pingki Datta S. K. Mahjour S. Faroughi 26 22 0 17 Dec 2022
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions F. Bonnet Jocelyn Ahmed Mazari Paola Cinnella Patrick Gallinari AI4CE 33 55 0 15 Dec 2022
Transfer Learning Enhanced DeepONet for Long-Time Prediction of Evolution Equations Wuzhe Xu Yulong Lu Li Wang 32 33 0 09 Dec 2022
Physics-guided Data Augmentation for Learning the Solution Operator of Linear Differential Equations Yemo Li Yiwen Pang Bin Shan AI4CE 31 3 0 08 Dec 2022
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems Derick Nganyu Tanyu Jianfeng Ning Tom Freudenberg Nick Heilenkötter A. Rademacher U. Iben Peter Maass AI4CE 23 34 0 06 Dec 2022
Fourier Continuation for Exact Derivative Computation in Physics-Informed Neural Operators Ha Maust Zong-Yi Li Yixuan Wang Daniel Leibovici O. Bruno T. Hou Anima Anandkumar AI4CE 29 11 0 29 Nov 2022
Replacing Automatic Differentiation by Sobolev Cubatures fastens Physics Informed Neural Nets and strengthens their Approximation Power Juan Esteban Suarez Cardona Michael Hecht 29 4 0 23 Nov 2022
Neural tangent kernel analysis of PINN for advection-diffusion equation M. Saadat B. Gjorgiev L. Das G. Sansavini 35 0 0 21 Nov 2022
Convergence analysis of unsupervised Legendre-Galerkin neural networks for linear second-order elliptic PDEs Seungchan Ko S. Yun Youngjoon Hong 30 5 0 16 Nov 2022
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition Yihang Gao Ka Chun Cheung Michael K. Ng 41 15 0 16 Nov 2022
Gradient-enhanced deep neural network approximations Xiaodong Feng Li Zeng UQCV 34 5 0 08 Nov 2022
A Deep Double Ritz Method (D $^2$ RM) for solving Partial Differential Equations using Neural Networks C. Uriarte David Pardo I. Muga J. Muñoz‐Matute 49 18 0 07 Nov 2022
An optimal control perspective on diffusion-based generative modeling Julius Berner Lorenz Richter Karen Ullrich DiffM 46 81 0 02 Nov 2022
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows Raphael Pellegrin Blake Bullwinkel M. Mattheakis P. Protopapas PINN AI4CE 34 9 0 01 Nov 2022
Convergence analysis of a quasi-Monte Carlo-based deep learning algorithm for solving partial differential equations Fengjiang Fu Xiaoqun Wang 29 2 0 28 Oct 2022
Partial Differential Equations Meet Deep Neural Networks: A Survey Shudong Huang Wentao Feng Chenwei Tang Jiancheng Lv AI4CE AIMat 32 18 0 27 Oct 2022
Mean-field neural networks: learning mappings on Wasserstein space H. Pham X. Warin 37 13 0 27 Oct 2022