Learning Neural Hamiltonian Dynamics: A Methodological Overview

Learning Neural Hamiltonian Dynamics: A Methodological Overview

28 February 2022

Papers citing "Learning Neural Hamiltonian Dynamics: A Methodological Overview"

11 / 11 papers shown

Title
Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics? Tae-Geun Kim Seong Chan Park 32 0 0 28 Oct 2024
Phase autoencoder for limit-cycle oscillators K. Yawata Kai Fukami Kunihiko Taira Hiroya Nakao 50 7 0 28 Feb 2024
Separable Hamiltonian Neural Networks Zi-Yu Khoo Dawen Wu Jonathan Sze Choong Low Stéphane Bressan 24 1 0 03 Sep 2023
Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints Alistair J R White Niki Kilbertus Maximilian Gelbrecht Niklas Boers 22 6 0 16 Jun 2023
Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models Sarvin Moradi N. Jaensson Roland Tóth Maarten Schoukens PINN 34 3 0 02 May 2023
Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems Quercus Hernandez Alberto Badías Francisco Chinesta Elías Cueto PINN AI4CE 34 13 0 03 Nov 2022
Thermodynamics of learning physical phenomena Elías Cueto Francisco Chinesta AI4CE 30 22 0 26 Jul 2022
Learning Hamiltonians of constrained mechanical systems E. Celledoni A. Leone Davide Murari B. Owren AI4CE 46 17 0 31 Jan 2022
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 271 2,315 0 18 Oct 2020
Symplectic Recurrent Neural Networks Zhengdao Chen Jianyu Zhang Martín Arjovsky Léon Bottou 152 221 0 29 Sep 2019
Input Convex Neural Networks Brandon Amos Lei Xu J. Zico Kolter 187 603 0 22 Sep 2016