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"

30 / 30 papers shown

Title
Fractional-Boundary-Regularized Deep Galerkin Method for Variational Inequalities in Mixed Optimal Stopping and Control Yun Zhao Harry Zheng 16 0 0 25 May 2025
A brief review of the Deep BSDE method for solving high-dimensional partial differential equations Jiequn Han Arnulf Jentzen Weinan E AI4CE 40 0 0 07 May 2025
Reliable and Efficient Inverse Analysis using Physics-Informed Neural Networks with Distance Functions and Adaptive Weight Tuning Shota Deguchi Mitsuteru Asai PINN AI4CE 99 0 0 25 Apr 2025
Verification and Validation for Trustworthy Scientific Machine Learning John D. Jakeman Lorena A. Barba J. Martins Thomas O'Leary-Roseberry AI4CE 78 0 0 21 Feb 2025
Quantum Recurrent Neural Networks with Encoder-Decoder for Time-Dependent Partial Differential Equations Yuan Chen Abdul Khaliq Khaled M. Furati AI4CE 119 0 0 20 Feb 2025
DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Yaohua Zang P. Koutsourelakis AI4CE 76 1 0 10 Feb 2025
Estimating Committor Functions via Deep Adaptive Sampling on Rare Transition Paths Yueyang Wang Kejun Tang Xili Wang Xiaoliang Wan Weiqing Ren Chao Yang 59 0 0 28 Jan 2025
Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Cyrus Neary Nathan Tsao Ufuk Topcu 97 1 0 15 Dec 2024
Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators Zekun Shi Zheyuan Hu Min Lin Kenji Kawaguchi 335 8 0 27 Nov 2024
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases Madison Cooley Varun Shankar Robert M. Kirby Shandian Zhe 62 2 0 04 Oct 2024
Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups Zakhar Shumaylov Peter Zaika James Rowbottom Ferdia Sherry Melanie Weber Carola-Bibiane Schönlieb 61 4 0 03 Oct 2024
Cauchy activation function and XNet Xin Li Zhihong Xia Hongkun Zhang 109 5 0 28 Sep 2024
Practical Aspects on Solving Differential Equations Using Deep Learning: A Primer Georgios Is. Detorakis 80 0 0 21 Aug 2024
Reduced-Order Neural Operators: Learning Lagrangian Dynamics on Highly Sparse Graphs Hrishikesh Viswanath Yue Chang Julius Berner Julius Berner Peter Yichen Chen Aniket Bera AI4CE 81 2 0 04 Jul 2024
Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs Ariel Neufeld Philipp Schmocker Sizhou Wu 63 7 0 08 May 2024
A score-based particle method for homogeneous Landau equation Yan Huang Li Wang OT 72 5 0 08 May 2024
A time-stepping deep gradient flow method for option pricing in (rough) diffusion models A. Papapantoleon Jasper Rou 49 2 0 01 Mar 2024
A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion models E. Georgoulis A. Papapantoleon Costas Smaragdakis 57 7 0 12 Jan 2024
Generating synthetic data for neural operators Erisa Hasani Rachel A. Ward AI4CE 90 8 0 04 Jan 2024
A Policy Gradient Framework for Stochastic Optimal Control Problems with Global Convergence Guarantee Mo Zhou Jian-Xiong Lu 61 8 0 11 Feb 2023
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations M. Raissi P. Perdikaris George Karniadakis PINN AI4CE 54 611 0 28 Nov 2017
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations M. Raissi P. Perdikaris George Karniadakis PINN AI4CE 65 912 0 28 Nov 2017
Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations C. Beck Weinan E Arnulf Jentzen 43 329 0 18 Sep 2017
Optimal approximation of piecewise smooth functions using deep ReLU neural networks P. Petersen Felix Voigtländer 162 473 0 15 Sep 2017
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations Weinan E Jiequn Han Arnulf Jentzen 107 790 0 15 Jun 2017
Deep Relaxation: partial differential equations for optimizing deep neural networks Pratik Chaudhari Adam M. Oberman Stanley Osher Stefano Soatto G. Carlier 122 153 0 17 Apr 2017
Stochastic Gradient Descent in Continuous Time Justin A. Sirignano K. Spiliopoulos 28 57 0 17 Nov 2016
Accelerating Eulerian Fluid Simulation With Convolutional Networks Jonathan Tompson Kristofer Schlachter Pablo Sprechmann Ken Perlin 75 530 0 13 Jul 2016
Training Very Deep Networks R. Srivastava Klaus Greff Jürgen Schmidhuber 96 1,675 0 22 Jul 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 815 149,474 0 22 Dec 2014