Forecasting Hamiltonian dynamics without canonical coordinates

Forecasting Hamiltonian dynamics without canonical coordinates

28 October 2020

Elliott G. Holliday

Scott T. Miller

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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 47 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