2 citations · 2 across the 3 of their papers we have counts for
6 papers · 1 filter
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…
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…
An adaptive Newton-based free-boundary Grad-Shafranov solver
Daniel A. Serino, Qi Tang, Xian-Zhu Tang +2
Equilibria in magnetic confinement devices result from force balancing between the Lorentz force and the plasma pressure gradient. In an axisymmetric configuration like a tokamak,…
A structure-preserving discontinuous Galerkin scheme for the Cahn-Hilliard equation including time adaptivity
Golo A. Wimmer, Ben S. Southworth, Qi Tang
We present a novel spatial discretization for the Cahn-Hilliard equation including transport. The method is given by a mixed discretization for the two elliptic operators, with the…