Forecasting Hamiltonian dynamics without canonical coordinates

Forecasting Hamiltonian dynamics without canonical coordinates

28 October 2020

Elliott G. Holliday

Scott T. Miller

ArXiv (abs)PDF HTML

Papers citing "Forecasting Hamiltonian dynamics without canonical coordinates"

11 / 11 papers shown

Title
Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify Framework Yubo Ye Sumeet Vadhavkar Xiajun Jiang R. Missel Huafeng Liu Linwei Wang 57 0 0 13 Mar 2024
Data-Driven Identification of Quadratic Representations for Nonlinear Hamiltonian Systems using Weakly Symplectic Liftings Süleyman Yıldız P. Goyal Thomas Bendokat P. Benner 81 10 0 02 Aug 2023
Learning unidirectional coupling using echo-state network S. Mandal M. Shrimali 76 7 0 23 Mar 2023
Generating extreme quantum scattering in graphene with machine learning Chen-Di Han Y. Lai 49 4 0 13 Dec 2022
Learning Trajectories of Hamiltonian Systems with Neural Networks Katsiaryna Haitsiukevich Alexander Ilin 51 4 0 11 Apr 2022
Neuronal diversity can improve machine learning for physics and beyond A. Choudhary Anil Radhakrishnan J. Lindner S. Sinha W. Ditto AI4CE 26 4 0 09 Apr 2022
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision I. Higgins Peter Wirnsberger Andrew Jaegle Aleksandar Botev 89 8 0 10 Nov 2021
Which priors matter? Benchmarking models for learning latent dynamics Aleksandar Botev Andrew Jaegle Peter Wirnsberger Daniel Hennes I. Higgins AI4CE 119 28 0 09 Nov 2021
Machine-Learning Non-Conservative Dynamics for New-Physics Detection Ziming Liu Bohan Wang Qi Meng Wei Chen M. Tegmark Tie-Yan Liu PINN AI4CE 111 15 0 31 May 2021
Learning Hamiltonian dynamics by reservoir computer Han Zhang Huawei Fan Liang Wang Xingang Wang 20 3 0 24 Apr 2021
Adaptable Hamiltonian neural networks Chen-Di Han Bryan Glaz Mulugeta Haile Y. Lai AI4TS 85 26 0 25 Feb 2021