Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data

3 December 2020

Yaofeng Desmond Zhong

Papers citing "Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data"

23 / 23 papers shown

Title
Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Cyrus Neary Nathan Tsao Ufuk Topcu 74 1 0 15 Dec 2024
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks S. Bishnoi Ravinder Bhattoo J. Jayadeva Sayan Ranu N. M. A. Krishnan PINN AI4CE 34 1 0 11 Jul 2023
Learning Latent Dynamics via Invariant Decomposition and (Spatio-)Temporal Transformers Kai Lagemann C. Lagemann Swarnava Mukherjee 34 2 0 21 Jun 2023
Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks Cyrus Neary Ufuk Topcu PINN AI4CE 8 12 0 01 Dec 2022
Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems Valentin Duruisseaux T. Duong Melvin Leok Nikolay A. Atanasov DRL AI4CE 13 12 0 29 Nov 2022
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems A. Thangamuthu Gunjan Kumar S. Bishnoi Ravinder Bhattoo N. M. A. Krishnan Sayan Ranu AI4CE PINN 32 22 0 10 Nov 2022
Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems Quercus Hernandez Alberto Badías Francisco Chinesta Elías Cueto PINN AI4CE 27 13 0 03 Nov 2022
Approximation of nearly-periodic symplectic maps via structure-preserving neural networks Valentin Duruisseaux J. Burby Q. Tang 30 11 0 11 Oct 2022
Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network Ravinder Bhattoo Sayan Ranu N. M. A. Krishnan AI4CE 28 17 0 23 Sep 2022
Learning the Dynamics of Particle-based Systems with Lagrangian Graph Neural Networks Ravinder Bhattoo Sayan Ranu N. M. A. Krishnan PINN AI4CE 28 20 0 03 Sep 2022
Thermodynamics of learning physical phenomena Elías Cueto Francisco Chinesta AI4CE 25 22 0 26 Jul 2022
A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic Barbara Z Cunha C. Droz A. Zine Stéphane Foulard M. Ichchou AI4CE 29 84 0 13 Apr 2022
Learning Neural Hamiltonian Dynamics: A Methodological Overview Zhijie Chen Mingquan Feng Junchi Yan H. Zha AI4CE 19 15 0 28 Feb 2022
Deconstructing the Inductive Biases of Hamiltonian Neural Networks Nate Gruver Marc Finzi Samuel Stanton A. Wilson AI4CE 15 39 0 10 Feb 2022
Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs Franck Djeumou Cyrus Neary Eric Goubault S. Putot Ufuk Topcu AI4TS 21 18 0 14 Jan 2022
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision I. Higgins Peter Wirnsberger Andrew Jaegle Aleksandar Botev 37 7 0 10 Nov 2021
Which priors matter? Benchmarking models for learning latent dynamics Aleksandar Botev Andrew Jaegle Peter Wirnsberger Daniel Hennes I. Higgins AI4CE 22 28 0 09 Nov 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
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling Kookjin Lee Nathaniel Trask P. Stinis 32 24 0 11 Sep 2021
Physics-Guided Deep Learning for Dynamical Systems: A Survey Rui Wang Rose Yu AI4CE PINN 39 64 0 02 Jul 2021
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models Yaofeng Desmond Zhong Biswadip Dey Amit Chakraborty 52 34 0 12 Feb 2021
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 130 422 0 10 Mar 2020
Symplectic Recurrent Neural Networks Zhengdao Chen Jianyu Zhang Martín Arjovsky Léon Bottou 146 219 0 29 Sep 2019