RODE-Net: Learning Ordinary Differential Equations with Randomness from Data

3 June 2020

Papers citing "RODE-Net: Learning Ordinary Differential Equations with Randomness from Data"

8 / 8 papers shown

Title
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 233 785 0 13 Mar 2020
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm Hao Xu Haibin Chang Dongxiao Zhang AI4CE 49 89 0 21 Jan 2020
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations Siyu Dai Shawn Schaffert Andreas G. Hofmann 120 365 0 05 Nov 2018
Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees Viraj Shah Chinmay Hegde GAN 89 166 0 23 Feb 2018
PDE-Net: Learning PDEs from Data Zichao Long Yiping Lu Xianzhong Ma Bin Dong DiffM AI4CE 43 756 0 26 Oct 2017
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations M. Raissi George Karniadakis AI4CE PINN 73 1,137 0 02 Aug 2017
Improved Training of Wasserstein GANs Ishaan Gulrajani Faruk Ahmed Martín Arjovsky Vincent Dumoulin Aaron Courville GAN 201 9,548 0 31 Mar 2017
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network C. Ledig Lucas Theis Ferenc Huszár Jose Caballero Andrew Cunningham ... Andrew P. Aitken Alykhan Tejani J. Totz Zehan Wang Wenzhe Shi GAN 242 10,686 0 15 Sep 2016