64 citations · 212 across the 21 of their papers we have counts for
3 papers · 2 filters
HEI: hybrid explicit-implicit learning for multiscale problems
Yalchin Efendiev, Wing Tat Leung, Guang Lin +1
Splitting is a method to handle application problems by splitting physics, scales, domain, and so on. Many splitting algorithms have been designed for efficient temporal discretiza…
Multi-variance replica exchange stochastic gradient MCMC for inverse and forward Bayesian physics-informed neural network
Guang Lin, Yating Wang, Zecheng Zhang
Physics-informed neural network (PINN) has been successfully applied in solving a variety of nonlinear non-convex forward and inverse problems. However, the training is challenging…
Flow-driven spectral chaos (FSC) method for long-time integration of second-order stochastic dynamical systems
Hugo Esquivel, Arun Prakash, Guang Lin
For decades, uncertainty quantification techniques based on the spectral approach have been demonstrated to be computationally more efficient than the Monte Carlo method for a wide…