A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations

13 March 2024

Papers citing "A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations"

6 / 6 papers shown

Title
Physics-informed neural networks for inverse problems in supersonic flows Ameya Dilip Jagtap Zhiping Mao Nikolaus Adams George Karniadakis PINN 43 221 0 23 Feb 2022
DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations Keju Tang Xiaoliang Wan Chao Yang 44 115 0 28 Dec 2021
SPINN: Sparse, Physics-based, and partially Interpretable Neural Networks for PDEs A. A. Ramabathiran P. Ramachandran PINN AI4CE 97 79 0 25 Feb 2021
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism L. McClenny U. Braga-Neto PINN 84 459 0 07 Sep 2020
Graph Convolutional Neural Networks for Web-Scale Recommender Systems Rex Ying Ruining He Kaifeng Chen Pong Eksombatchai William L. Hamilton J. Leskovec GNN BDL 266 3,550 0 06 Jun 2018
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification Rohit Tripathy Ilias Bilionis AI4CE 65 409 0 02 Feb 2018