8 papers
Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks
Yongsheng Chen, Wei Guo, Qi Tang +1
We introduce a novel data-driven symplectic induced-order modeling (ROM) framework for high-dimensional Hamiltonian systems that unifies latent-space discovery and dynamics learnin…
A Structure-Preserving Decorated Particle Method for the Vlasov-Poisson System
Mandela B. Quashie, J. W. Burby, Andrew J. Christlieb +1
We revisit the Scovel-Weinstein framework (Scovel & Weinstein, CPAM 1994) for reducing the Vlasov-Poisson system while preserving its Hamiltonian structure. Standard particle-in-ce…
Self-mediation of runaway electrons via self-excited wave-wave and wave-particle interactions
Qile Zhang, Yanzeng Zhang, Qi Tang +1
Nonlinear dynamics of runaway electron induced wave instabilities can significantly modify the runaway distribution critical to tokamak operations. Here we present the first-ever f…
Learning Generalized Diffusions using an Energetic Variational Approach
Yubin Lu, Xiaofan Li, Chun Liu +2
Extracting governing physical laws from computational or experimental data is crucial across various fields such as fluid dynamics and plasma physics. Many of those physical laws a…
Structure-Preserving Neural Ordinary Differential Equations for Stiff Systems
Allen Alvarez Loya, Daniel A. Serino, J. W. Burby +1
Neural ordinary differential equations (NODEs) are an effective approach for data-driven modeling of dynamical systems arising from simulations and experiments. One of the major sh…
Structure-Preserving Transfer of Grad-Shafranov Equilibria to Magnetohydrodynamic Solvers
Rushan Zhang, Golo Wimmer, Qi Tang
Magnetohydrodynamic (MHD) solvers used to study dynamic plasmas for magnetic confinement fusion typically rely on initial conditions that describe force balance, which are provided…