Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

27 May 2019

Papers citing "Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning"

8 / 8 papers shown

Title
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
Neural Partial Differential Equations with Functional Convolution Z. Wu Xingzhe He Yijun Li Cheng Yang Rui Liu S. Xiong Bo Zhu 23 1 0 10 Mar 2023
Multi-Agent Learning of Numerical Methods for Hyperbolic PDEs with Factored Dec-MDP Yiwei Fu Dheeraj Kapilavai E. Way 27 1 0 31 May 2022
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning Chuizheng Meng Sungyong Seo Defu Cao Sam Griesemer Yan Liu PINN AI4CE 53 57 0 31 Mar 2022
AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations Ziming Liu Varun Madhavan M. Tegmark PINN 38 27 0 23 Mar 2022
Cell-average based neural network method for hyperbolic and parabolic partial differential equations Changxin Qiu Jue Yan 21 10 0 02 Jul 2021
Data-Driven Deep Learning of Partial Differential Equations in Modal Space Kailiang Wu D. Xiu 20 149 0 15 Oct 2019
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 496 11,727 0 09 Mar 2017