82 citations · 110 across the 6 of their papers we have counts for
6 papers
Structure-Aware Variational Learning of a Class of Generalized Diffusions
Yubin Lu, Xiaofan Li, Chun Liu +2
Learning the underlying potential energy of stochastic gradient systems from partial and noisy observations is a fundamental problem arising in physics, chemistry, and data-driven…
Approximation of nearly-periodic symplectic maps via structure-preserving neural networks
Valentin Duruisseaux, Joshua W. Burby, Qi Tang
A continuous-time dynamical system with parameter is nearly-periodic if all its trajectories are periodic with nowhere-vanishing angular frequency as ap…
Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems
Alec J. Linot, Joshua W. Burby, Qi Tang +3
In data-driven modeling of spatiotemporal phenomena careful consideration often needs to be made in capturing the dynamics of the high wavenumbers. This problem becomes especially…
Efficient training of artificial neural network surrogates for a collisional-radiative model through adaptive parameter space sampling
Nathan A. Garland, Romit Maulik, Qi Tang +2
Reliable plasma transport modeling for magnetic confinement fusion depends on accurately resolving the ion charge state distribution and radiative power losses of the plasma. These…
Fast neural Poincaré maps for toroidal magnetic fields
J. W. Burby, Q. Tang, R. Maulik
Poincaré maps for toroidal magnetic fields are routinely employed to study gross confinement properties in devices built to contain hot plasmas. In most practical applications, eva…
Entropy-based closure for probabilistic learning on manifolds
C. Soizea, R. Ghanem, C. Safta +7
In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically cons…