Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

28 November 2017

Papers citing "Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations"

8 / 8 papers shown

Title
Variational autoencoders with latent high-dimensional steady geometric flows for dynamics Andrew Gracyk DRL 108 0 0 03 Jan 2025
HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Eric Hedlin Munawar Hayat Fatih Porikli Kwang Moo Yi Shweta Mahajan 3DH 109 0 0 22 Dec 2024
Phase Diagram from Nonlinear Interaction between Superconducting Order and Density: Toward Data-Based Holographic Superconductor Sejin Kim Kyung Kiu Kim Yunseok Seo 30 0 0 09 Oct 2024
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases Madison Cooley Varun Shankar Robert M. Kirby Shandian Zhe 64 2 0 04 Oct 2024
MINN: Learning the dynamics of differential-algebraic equations and application to battery modeling Yicun Huang Changfu Zou Yongqian Li T. Wik PINN 45 10 0 27 Apr 2023
I Know Therefore I Score: Label-Free Crafting of Scoring Functions using Constraints Based on Domain Expertise Ragja Palakkadavath S. Sivaprasad Shirish S. Karande N. Pedanekar 34 0 0 18 Mar 2022
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs Liu Yang Sean Treichler Thorsten Kurth Keno Fischer D. Barajas-Solano ... Valentin Churavy A. Tartakovsky Michael Houston P. Prabhat George Karniadakis AI4CE 61 38 0 29 Oct 2019
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations M. Raissi George Karniadakis AI4CE PINN 55 1,134 0 02 Aug 2017