Area of research
Statistical and Nonlinear Physics · Artificial Intelligence
Research interest
Research interests include Artificial neural network, Statistical physics, Computer science, Applied mathematics, Curse of dimensionality, and Partial differential equation.
Tensor neural networks for high-dimensional Fokker–Planck equations
Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker–Planck Equations
Tackling the curse of dimensionality with physics-informed neural networks
Hutchinson Trace Estimation for high-dimensional and high-order Physics-Informed Neural Networks
Tackling the curse of dimensionality in fractional and tempered fractional PDEs with physics-informed neural networks
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?