Structure-preserving Method for Reconstructing Unknown Hamiltonian Systems from Trajectory Data

24 May 2019

Papers citing "Structure-preserving Method for Reconstructing Unknown Hamiltonian Systems from Trajectory Data"

11 / 11 papers shown

Title
Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling Nicholas Galioto Harsh Sharma Boris Kramer Alex Arkady Gorodetsky 44 0 0 23 Jan 2024
Bayesian Identification of Nonseparable Hamiltonian Systems Using Stochastic Dynamic Models Harsh Sharma Nicholas Galioto Alex A. Gorodetsky Boris Kramer 41 3 0 15 Sep 2022
VPNets: Volume-preserving neural networks for learning source-free dynamics Aiqing Zhu Beibei Zhu Jiawei Zhang Yifa Tang Jian-Dong Liu 34 3 0 29 Apr 2022
Modeling unknown dynamical systems with hidden parameters Xiaohan Fu Weize Mao L. Chang D. Xiu 24 5 0 03 Feb 2022
Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks Tian Zheng Weihao Gao Chong-Jun Wang AI4CE 42 3 0 30 Nov 2021
Deep Neural Network Modeling of Unknown Partial Differential Equations in Nodal Space Zhen Chen V. Churchill Kailiang Wu D. Xiu AI4CE 14 47 0 07 Jun 2021
Learning reduced systems via deep neural networks with memory Xiaohang Fu L. Chang D. Xiu 11 32 0 20 Mar 2020
On generalized residue network for deep learning of unknown dynamical systems Zhen Chen D. Xiu AI4CE 19 46 0 23 Jan 2020
Machine learning and serving of discrete field theories -- when artificial intelligence meets the discrete universe H. Qin 32 30 0 22 Oct 2019
Data-Driven Deep Learning of Partial Differential Equations in Modal Space Kailiang Wu D. Xiu 11 149 0 15 Oct 2019
D3M: A deep domain decomposition method for partial differential equations Ke Li Keju Tang Tianfan Wu Qifeng Liao AI4CE 22 114 0 24 Sep 2019