15 citations · 18 across the 7 of their papers we have counts for
8 papers
A multi-physics structure-preserving integrator with uniform error bounds for relativistic charged-particle dynamics under strong magnetic fields
Mengting Hu, Yifa Tang, Bin Wang
In this paper, we develop an explicit multi-physics structure-preserving Strang splitting scheme for a four-dimensional relativistic charged-particle dynamical system in the presen…
CSympNet-ID: conformal-symplectic map learning for linearly damped Hamiltonian systems
Jiale Gong, Pengzhan Jin, Dongyang Kuang +2
Learning dissipative dynamics from discrete observations is essential for reliable long-horizon prediction and physically meaningful parameter identification. For linearly damped H…
Learning symplectic model reduction based on an approximation theorem of symplectic embeddings
Liyi Feng, Yifa Tang, Yulin Xie +2
High-dimensional Hamiltonian systems play a central role in many scientific and engineering disciplines, with dynamics that evolve on symplectic manifolds. Although deep learning p…
A deformation-based framework for learning solution mappings of PDEs defined on varying domains
Shanshan Xiao, Pengzhan Jin, Yifa Tang
In this work, we establish a deformation-based framework for learning solution mappings of PDEs defined on varying domains. The union of functions defined on varying domains can be…
Learning solution operators of PDEs defined on varying domains via MIONet
Shanshan Xiao, Pengzhan Jin, Yifa Tang
In this work, we propose a method to learn the solution operators of PDEs defined on varying domains via MIONet, and theoretically justify this method. We first extend the approxim…
Canonical Hamiltonian Guiding Center Dynamics and Its Intrinsic Magnetic Moment
Ruili Zhang, Jian Liu, Tong Liu +3
The concept of guiding center is potent in astrophysics, space plasmas, fusion researches, and arc plasmas to solve the multi-scale dynamics of magnetized plasmas. In this letter,…
Generalized Lagrangian Neural Networks
Shanshan Xiao, Jiawei Zhang, Yifa Tang
Incorporating neural networks for the solution of Ordinary Differential Equations (ODEs) represents a pivotal research direction within computational mathematics. Within neural net…
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness
Pengzhan Jin, Lu Lu, Yifa Tang +1
The accuracy of deep learning, i.e., deep neural networks, can be characterized by dividing the total error into three main types: approximation error, optimization error, and gene…