Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

26 September 2019

Yaofeng Desmond Zhong

Papers citing "Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control"

20 / 170 papers shown

Title
Machine Learning a Molecular Hamiltonian for Predicting Electron Dynamics Harish S. Bhat Karnamohit Ranka Christine M Isborn 14 12 0 19 Jul 2020
A Differential Game Theoretic Neural Optimizer for Training Residual Networks Guan-Horng Liu T. Chen Evangelos A. Theodorou 14 2 0 17 Jul 2020
Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control Yaofeng Desmond Zhong Naomi Ehrich Leonard DRL AI4CE 17 43 0 03 Jul 2020
Deep learning of thermodynamics-aware reduced-order models from data Quercus Hernandez Alberto Badías D. González Francisco Chinesta Elías Cueto PINN AI4CE 10 79 0 03 Jul 2020
Learning Potentials of Quantum Systems using Deep Neural Networks Arijit Sehanobish H. Corzo Onur Kara David van Dijk 6 12 0 23 Jun 2020
On Second Order Behaviour in Augmented Neural ODEs Alexander Norcliffe Cristian Bodnar Ben Day Nikola Simidjievski Pietro Lió 23 89 0 12 Jun 2020
Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics Manuel A. Roehrl Thomas Runkler Veronika Brandtstetter Michel Tokic Stefan Obermayer PINN 11 77 0 29 May 2020
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J. Willard X. Jia Shaoming Xu M. Steinbach Vipin Kumar AI4CE 88 387 0 10 Mar 2020
Forecasting Sequential Data using Consistent Koopman Autoencoders Omri Azencot N. Benjamin Erichson Vanessa Lin Michael W. Mahoney AI4TS AI4CE 8 141 0 04 Mar 2020
Differentiable Molecular Simulations for Control and Learning Wujie Wang Simon Axelrod Rafael Gómez-Bombarelli AI4CE 100 49 0 27 Feb 2020
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data Marc Finzi Samuel Stanton Pavel Izmailov A. Wilson 11 316 0 25 Feb 2020
Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning Yaofeng Desmond Zhong Biswadip Dey Amit Chakraborty PINN AI4CE 21 78 0 20 Feb 2020
DDPNOpt: Differential Dynamic Programming Neural Optimizer Guan-Horng Liu T. Chen Evangelos A. Theodorou 6 7 0 20 Feb 2020
Linearly Constrained Neural Networks J. Hendriks Carl Jidling A. Wills Thomas B. Schon 8 33 0 05 Feb 2020
Universal Differential Equations for Scientific Machine Learning Christopher Rackauckas Yingbo Ma Julius Martensen Collin Warner K. Zubov R. Supekar Dominic J. Skinner Ali Ramadhan Alan Edelman AI4CE 27 568 0 13 Jan 2020
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems Pengzhan Jin Zhen Zhang Aiqing Zhu Yifa Tang George Karniadakis 13 21 0 11 Jan 2020
Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework Wanxin Jin Zhaoran Wang Zhuoran Yang Shaoshuai Mou 20 77 0 30 Dec 2019
Machine learning and serving of discrete field theories -- when artificial intelligence meets the discrete universe H. Qin 16 30 0 22 Oct 2019
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 43 2,009 0 08 Oct 2019
Neural Canonical Transformation with Symplectic Flows Shuo-Hui Li Chen Dong Linfeng Zhang Lei Wang DRL 18 28 0 30 Sep 2019