Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

26 September 2019

Yaofeng Desmond Zhong

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

50 / 170 papers shown

Title
Which priors matter? Benchmarking models for learning latent dynamics Aleksandar Botev Andrew Jaegle Peter Wirnsberger Daniel Hennes I. Higgins AI4CE 16 28 0 09 Nov 2021
Constructing Neural Network-Based Models for Simulating Dynamical Systems Christian Møldrup Legaard Thomas Schranz G. Schweiger Ján Drgovna Basak Falay C. Gomes Alexandros Iosifidis M. Abkar P. Larsen PINN AI4CE 28 93 0 02 Nov 2021
Can we learn gradients by Hamiltonian Neural Networks? A. Timofeev A. Afonin Yehao Liu 12 0 0 31 Oct 2021
A Differentiable Newton-Euler Algorithm for Real-World Robotics M. Lutter Vallijah Subasri Joe Watson Frank Rudzicz 22 7 0 24 Oct 2021
Heavy Ball Neural Ordinary Differential Equations Hedi Xia Vai Suliafu H. Ji T. Nguyen Andrea L. Bertozzi Stanley J. Osher Bao Wang 25 55 0 10 Oct 2021
A Review of Physics-based Machine Learning in Civil Engineering S. Vadyala S. N. Betgeri J. Matthews Elizabeth Matthews AI4CE 25 152 0 09 Oct 2021
Lagrangian Neural Network with Differentiable Symmetries and Relational Inductive Bias Ravinder Bhattoo Sayan Ranu N. M. A. Krishnan AI4CE 47 4 0 07 Oct 2021
Continuous-Time Fitted Value Iteration for Robust Policies M. Lutter Boris Belousov Shie Mannor D. Fox Animesh Garg Jan Peters 8 9 0 05 Oct 2021
Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models M. Lutter Jan Peters PINN AI4CE 31 39 0 05 Oct 2021
Second-Order Neural ODE Optimizer Guan-Horng Liu T. Chen Evangelos A. Theodorou 21 11 0 29 Sep 2021
Locally-symplectic neural networks for learning volume-preserving dynamics J. Bajārs 20 9 0 19 Sep 2021
Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling Franck Djeumou Cyrus Neary Eric Goubault S. Putot Ufuk Topcu PINN AI4CE 39 68 0 14 Sep 2021
ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting Yu Huang James Li Min Shi H. Zhuang Xingquan Zhu Laurent Chérubin James H. VanZwieten Yufei Tang AI4CE PINN 23 6 0 12 Aug 2021
Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations Xing Chen Flavio Abreu Araujo M. Riou J. Torrejon D. Ravelosona W. Kang Weisheng Zhao Julie Grollier D. Querlioz 15 40 0 23 Jul 2021
Physics perception in sloshing scenes with guaranteed thermodynamic consistency B. Moya Alberto Badías D. González Francisco Chinesta Elías Cueto 17 14 0 24 Jun 2021
Symplectic Learning for Hamiltonian Neural Networks M. David Florian Méhats 11 34 0 22 Jun 2021
Stateful ODE-Nets using Basis Function Expansions A. Queiruga N. Benjamin Erichson Liam Hodgkinson Michael W. Mahoney 27 16 0 21 Jun 2021
Scalars are universal: Equivariant machine learning, structured like classical physics Soledad Villar D. Hogg Kate Storey-Fisher Weichi Yao Ben Blum-Smith PINN AI4CE 24 130 0 11 Jun 2021
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression Zhaozhi Qian W. Zame L. Fleuren Paul Elbers M. Schaar OOD 19 53 0 05 Jun 2021
Weak Form Generalized Hamiltonian Learning Kevin Course Trefor W. Evans P. Nair AI4CE 11 9 0 11 Apr 2021
Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation and Optimal Measurement Placement in Power Systems? A. Afonin Michael Chertkov 17 3 0 07 Apr 2021
gradSim: Differentiable simulation for system identification and visuomotor control Krishna Murthy Jatavallabhula Miles Macklin Florian Golemo Vikram S. Voleti Petrini ... Erleben Liam Paull Florian Shkurti Derek Nowrouzezahrai Sanja Fidler 19 97 0 06 Apr 2021
No Need for Interactions: Robust Model-Based Imitation Learning using Neural ODE HaoChih Lin Baopu Li Xin Zhou Jiankun Wang M. Meng 14 6 0 03 Apr 2021
Implicit energy regularization of neural ordinary-differential-equation control Lucas Böttcher Nino Antulov-Fantulin Thomas Asikis 21 66 0 11 Mar 2021
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps Ren-Chuen Chen Molei Tao 22 49 0 09 Mar 2021
Identifying Physical Law of Hamiltonian Systems via Meta-Learning Seungjun Lee Haesang Yang W. Seong 22 13 0 23 Feb 2021
KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-Zero Training Loss Yu-Hsueh Chen Takashi Matsubara Takaharu Yaguchi 14 4 0 22 Feb 2021
A Differential Geometry Perspective on Orthogonal Recurrent Models Omri Azencot N. Benjamin Erichson M. Ben-Chen Michael W. Mahoney AI4CE 6 5 0 18 Feb 2021
Group Equivariant Conditional Neural Processes M. Kawano Wataru Kumagai Akiyoshi Sannai Yusuke Iwasawa Y. Matsuo BDL 45 20 0 17 Feb 2021
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models Yaofeng Desmond Zhong Biswadip Dey Amit Chakraborty 52 34 0 12 Feb 2021
Noisy Recurrent Neural Networks S. H. Lim N. Benjamin Erichson Liam Hodgkinson Michael W. Mahoney 4 52 0 09 Feb 2021
Continuous-Time Model-Based Reinforcement Learning Çağatay Yıldız Markus Heinonen Harri Lähdesmäki OffRL 8 52 0 09 Feb 2021
MALI: A memory efficient and reverse accurate integrator for Neural ODEs Juntang Zhuang Nicha Dvornek S. Tatikonda James S. Duncan 22 49 0 09 Feb 2021
Enhancing Human-Machine Teaming for Medical Prognosis Through Neural Ordinary Differential Equations (NODEs) D. A. Fompeyrine E. Vorm N. Ricka F. Rose G. Pellegrin 22 6 0 08 Feb 2021
Structure-preserving Gaussian Process Dynamics K. Ensinger Friedrich Solowjow Sebastian Ziesche Michael Tiemann Sebastian Trimpe 21 9 0 02 Feb 2021
Optimal Energy Shaping via Neural Approximators Stefano Massaroli Michael Poli Federico Califano Jinkyoo Park Atsushi Yamashita Hajime Asama 13 12 0 14 Jan 2021
LieTransformer: Equivariant self-attention for Lie Groups M. Hutchinson Charline Le Lan Sheheryar Zaidi Emilien Dupont Yee Whye Teh Hyunjik Kim 12 111 0 20 Dec 2020
Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks Pengzhan Jin Zhen Zhang Ioannis G. Kevrekidis George Karniadakis 28 50 0 05 Dec 2020
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data Yaofeng Desmond Zhong Biswadip Dey Amit Chakraborty PINN AI4CE 17 47 0 03 Dec 2020
Nested Mixture of Experts: Cooperative and Competitive Learning of Hybrid Dynamical System Junhyeok Ahn Luis Sentis 18 3 0 20 Nov 2020
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning M. Lutter Johannes Silberbauer Joe Watson Jan Peters OffRL 29 33 0 03 Nov 2020
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints Marc Finzi Ke Alexander Wang A. Wilson AI4CE 18 126 0 26 Oct 2020
LagNetViP: A Lagrangian Neural Network for Video Prediction Christine Allen-Blanchette Sushant Veer Anirudha Majumdar Naomi Ehrich Leonard 28 30 0 24 Oct 2020
Nonseparable Symplectic Neural Networks S. Xiong Yunjin Tong Xingzhe He Shuqi Yang Cheng Yang Bo Zhu 11 32 0 23 Oct 2020
Scalable Graph Networks for Particle Simulations Karolis Martinkus Aurélien Lucchi Nathanael Perraudin AI4CE 17 11 0 14 Oct 2020
Learning Thermodynamically Stable and Galilean Invariant Partial Differential Equations for Non-equilibrium Flows Juntao Huang Zhiting Ma Y. Zhou W. Yong AI4CE 25 16 0 28 Sep 2020
"Hey, that's not an ODE": Faster ODE Adjoints via Seminorms Patrick Kidger Ricky T. Q. Chen Terry Lyons 16 39 0 20 Sep 2020
OnsagerNet: Learning Stable and Interpretable Dynamics using a Generalized Onsager Principle Haijun Yu Xinyuan Tian Weinan E Qianxiao Li AI4CE 12 42 0 06 Sep 2020
Continuous-in-Depth Neural Networks A. Queiruga N. Benjamin Erichson D. Taylor Michael W. Mahoney 13 45 0 05 Aug 2020
Time-Reversal Symmetric ODE Network In Huh Eunho Yang Sung Ju Hwang Jinwoo Shin 6 19 0 22 Jul 2020