Symplectic Recurrent Neural Networks

29 September 2019

Papers citing "Symplectic Recurrent Neural Networks"

50 / 54 papers shown

Title
Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery Yana Lishkova P. Scherer Steffen Ridderbusch M. Jamnik Pietro Lio' Sina Ober-Blobaum Christian Offen PINN 63 7 0 28 Jan 2025
Efficiently Parameterized Neural Metriplectic Systems Anthony Gruber Kookjin Lee Haksoo Lim Noseong Park Nathaniel Trask 58 1 0 28 Jan 2025
Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling Nicholas Galioto Harsh Sharma Boris Kramer Alex Arkady Gorodetsky 36 0 0 23 Jan 2024
Port-Hamiltonian Neural ODE Networks on Lie Groups For Robot Dynamics Learning and Control T. Duong Abdullah Altawaitan Jason Stanley Nikolay A. Atanasov 23 10 0 17 Jan 2024
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach Kai Zhao Qiyu Kang Yang Song Rui She Sijie Wang Wee Peng Tay AAML 35 22 0 10 Oct 2023
Learning Hybrid Dynamics Models With Simulator-Informed Latent States K. Ensinger Sebastian Ziesche Sebastian Trimpe 29 1 0 06 Sep 2023
Hamiltonian GAN Christine Allen-Blanchette GAN AI4CE 25 1 0 22 Aug 2023
Learning Dynamical Systems from Noisy Data with Inverse-Explicit Integrators Haakon Noren Sølve Eidnes E. Celledoni 21 3 0 06 Jun 2023
Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models Sarvin Moradi N. Jaensson Roland Tóth Maarten Schoukens PINN 27 3 0 02 May 2023
Pseudo-Hamiltonian neural networks for learning partial differential equations Sølve Eidnes K. Lye 18 10 0 27 Apr 2023
Constraining Chaos: Enforcing dynamical invariants in the training of recurrent neural networks Jason A. Platt S. Penny T. A. Smith Tse-Chun Chen H. Abarbanel AI4TS 28 5 0 24 Apr 2023
Learning Hamiltonian Systems with Mono-Implicit Runge-Kutta Methods Haakon Noren 19 3 0 07 Mar 2023
Node Embedding from Hamiltonian Information Propagation in Graph Neural Networks Qiyu Kang Kai Zhao Yang Song Sijie Wang Rui She Wee Peng Tay 35 0 0 02 Mar 2023
Learning PDE Solution Operator for Continuous Modeling of Time-Series Yesom Park Jaemoo Choi Changyeon Yoon Changhoon Song Myung-joo Kang AI4TS AI4CE 18 3 0 02 Feb 2023
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions F. Bonnet Jocelyn Ahmed Mazari Paola Cinnella Patrick Gallinari AI4CE 25 54 0 15 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 8 11 0 29 Nov 2022
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing Salah A. Faroughi N. Pawar C. Fernandes Maziar Raissi Subasish Das N. Kalantari S. K. Mahjour PINN AI4CE 27 48 0 14 Nov 2022
Thermodynamics-informed neural networks for physically realistic mixed reality Quercus Hernandez Alberto Badías Francisco Chinesta Elías Cueto PINN AI4CE 17 16 0 24 Oct 2022
Approximation of nearly-periodic symplectic maps via structure-preserving neural networks Valentin Duruisseaux J. Burby Q. Tang 28 11 0 11 Oct 2022
Dynamical systems' based neural networks E. Celledoni Davide Murari B. Owren Carola-Bibiane Schönlieb Ferdia Sherry OOD 38 10 0 05 Oct 2022
Exact conservation laws for neural network integrators of dynamical systems E. Müller PINN 39 12 0 23 Sep 2022
Learning Interpretable Dynamics from Images of a Freely Rotating 3D Rigid Body J. Mason Christine Allen-Blanchette Nicholas Zolman Elizabeth Davison Naomi Ehrich Leonard 3DH AI4CE 33 8 0 23 Sep 2022
Bayesian Identification of Nonseparable Hamiltonian Systems Using Stochastic Dynamic Models Harsh Sharma Nicholas Galioto Alex A. Gorodetsky Boris Kramer 27 3 0 15 Sep 2022
Constants of motion network M. F. Kasim Yi Heng Lim 10 4 0 22 Aug 2022
Estimating a potential without the agony of the partition function E. Haber Moshe Eliasof L. Tenorio 25 2 0 19 Aug 2022
Thermodynamics of learning physical phenomena Elías Cueto Francisco Chinesta AI4CE 23 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 27 84 0 13 Apr 2022
Thermodynamics-informed graph neural networks Quercus Hernandez Alberto Badías Francisco Chinesta Elías Cueto AI4CE PINN 27 31 0 03 Mar 2022
Learning Neural Hamiltonian Dynamics: A Methodological Overview Zhijie Chen Mingquan Feng Junchi Yan H. Zha AI4CE 19 15 0 28 Feb 2022
Learning continuous models for continuous physics Aditi S. Krishnapriyan A. Queiruga N. Benjamin Erichson Michael W. Mahoney AI4CE 21 32 0 17 Feb 2022
Deconstructing the Inductive Biases of Hamiltonian Neural Networks Nate Gruver Marc Finzi Samuel Stanton A. Wilson AI4CE 13 39 0 10 Feb 2022
Learning Hamiltonians of constrained mechanical systems E. Celledoni A. Leone Davide Murari B. Owren AI4CE 36 17 0 31 Jan 2022
Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately A. Sosanya S. Greydanus PINN AI4CE 35 26 0 25 Jan 2022
Symplectic Momentum Neural Networks -- Using Discrete Variational Mechanics as a prior in Deep Learning Saul Santos Monica Ekal R. Ventura 19 5 0 20 Jan 2022
Constraint-based graph network simulator Yulia Rubanova Alvaro Sanchez-Gonzalez Tobias Pfaff Peter W. Battaglia PINN AI4CE 25 28 0 16 Dec 2021
Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks Tian Zheng Weihao Gao Chong-Jun Wang AI4CE 21 3 0 30 Nov 2021
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision I. Higgins Peter Wirnsberger Andrew Jaegle Aleksandar Botev 34 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 14 28 0 09 Nov 2021
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling Kookjin Lee Nathaniel Trask P. Stinis 30 24 0 11 Sep 2021
An Extensible Benchmark Suite for Learning to Simulate Physical Systems Karl Otness Arvi Gjoka Joan Bruna Daniele Panozzo Benjamin Peherstorfer T. Schneider Denis Zorin 19 23 0 09 Aug 2021
Machine learning structure preserving brackets for forecasting irreversible processes Kookjin Lee Nathaniel Trask P. Stinis AI4CE 31 42 0 23 Jun 2021
Symplectic Learning for Hamiltonian Neural Networks M. David Florian Méhats 11 34 0 22 Jun 2021
Adaptable Hamiltonian neural networks Chen-Di Han Bryan Glaz Mulugeta Haile Y. Lai AI4TS 9 25 0 25 Feb 2021
Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory Takashi Matsubara Yuto Miyatake Takaharu Yaguchi 15 23 0 19 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
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting Yuan Yin Vincent Le Guen Jérémie Donà Emmanuel de Bézenac Ibrahim Ayed Nicolas Thome Patrick Gallinari AI4CE PINN 27 132 0 09 Oct 2020
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies T. Konstantin Rusch Siddhartha Mishra 19 88 0 02 Oct 2020
Learning Physical Constraints with Neural Projections Shuqi Yang Xingzhe He Bo Zhu 3DV AI4CE 20 25 0 23 Jun 2020
Lipschitz Recurrent Neural Networks N. Benjamin Erichson Omri Azencot A. Queiruga Liam Hodgkinson Michael W. Mahoney 22 107 0 22 Jun 2020
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 130 422 0 10 Mar 2020