Constraining Chaos: Enforcing dynamical invariants in the training of recurrent neural networks

24 April 2023

Papers citing "Constraining Chaos: Enforcing dynamical invariants in the training of recurrent neural networks"

6 / 6 papers shown

Title
DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems Yair Schiff Zhong Yi Wan Jeffrey B. Parker Stephan Hoyer Volodymyr Kuleshov Fei Sha Leonardo Zepeda-Núnez 15 11 0 06 Feb 2024
Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence T. A. Smith S. Penny Jason A. Platt Tse-Chun Chen 15 5 0 28 Apr 2023
A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics Jason A. Platt S. Penny T. A. Smith Tse-Chun Chen H. Abarbanel AI4TS 16 32 0 21 Jan 2022
Noether Networks: Meta-Learning Useful Conserved Quantities Ferran Alet Dylan D. Doblar Allan Zhou J. Tenenbaum Kenji Kawaguchi Chelsea Finn 58 26 0 06 Dec 2021
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 170 755 0 13 Mar 2020
Symplectic Recurrent Neural Networks Zhengdao Chen Jianyu Zhang Martín Arjovsky Léon Bottou 139 219 0 29 Sep 2019