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2012.02334
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Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data
Conference on Learning for Dynamics & Control (L4DC), 2020
3 December 2020
Yaofeng Desmond Zhong
Biswadip Dey
Amit Chakraborty
PINN
AI4CE
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Papers citing
"Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data"
36 / 36 papers shown
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Lagrangian neural networks for nonholonomic mechanics
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Stability-Informed Initialization of Neural Ordinary Differential Equations
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Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks
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11 Jul 2023
Learning Latent Dynamics via Invariant Decomposition and (Spatio-)Temporal Transformers
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Graph Neural Stochastic Differential Equations for Learning Brownian Dynamics
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How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations
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Discovering interpretable Lagrangian of dynamical systems from data
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09 Feb 2023
Physics-Informed Model-Based Reinforcement Learning
Conference on Learning for Dynamics & Control (L4DC), 2022
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05 Dec 2022
Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks
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Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems
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Melvin Leok
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Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems
Neural Information Processing Systems (NeurIPS), 2022
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Jignesh M. Patel
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10 Nov 2022
Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems
Computational Mechanics (Comput. Mech.), 2022
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Francisco Chinesta
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Approximation of nearly-periodic symplectic maps via structure-preserving neural networks
Scientific Reports (Sci Rep), 2022
Valentin Duruisseaux
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Q. Tang
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11 Oct 2022
Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian
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Gongjie Li
Evangelos A. Theodorou
Molei Tao
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243
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30 Sep 2022
Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network
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Jignesh M. Patel
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292
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Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems
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331
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Thermodynamics of learning physical phenomena
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A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic
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C. Droz
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Stéphane Foulard
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271
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13 Apr 2022
Learning Trajectories of Hamiltonian Systems with Neural Networks
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Katsiaryna Haitsiukevich
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Learning Neural Hamiltonian Dynamics: A Methodological Overview
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Mingquan Feng
Junchi Yan
H. Zha
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244
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Deconstructing the Inductive Biases of Hamiltonian Neural Networks
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Nate Gruver
Marc Finzi
Samuel Stanton
A. Wilson
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271
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10 Feb 2022
Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs
International Joint Conference on Artificial Intelligence (IJCAI), 2022
Franck Djeumou
Cyrus Neary
Eric Goubault
S. Putot
Ufuk Topcu
AI4TS
270
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14 Jan 2022
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision
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I. Higgins
Peter Wirnsberger
Andrew Jaegle
Aleksandar Botev
276
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10 Nov 2021
Which priors matter? Benchmarking models for learning latent dynamics
Aleksandar Botev
Andrew Jaegle
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Daniel Hennes
I. Higgins
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Lagrangian Neural Network with Differentiable Symmetries and Relational Inductive Bias
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Jignesh M. Patel
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Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
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Cyrus Neary
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Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling
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Physics-Guided Deep Learning for Dynamical Systems: A Survey
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Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models
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Yaofeng Desmond Zhong
Biswadip Dey
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324
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Optimal Energy Shaping via Neural Approximators
SIAM Journal on Applied Dynamical Systems (SIADS), 2021
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Michael Poli
Federico Califano
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Atsushi Yamashita
Hajime Asama
135
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14 Jan 2021
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