Rate My Professor Xiaoliang Wan

Professor Wan's research specializations include scientific machine learning, stochastic modeling, numerical methods for stochastic partial differential equations, adaptivity in numerical simulations, and the minimum action method for large deviation principles. Additional interests encompass numerical methods for uncertainty quantification, minimum action methods for rare event simulation, adaptive numerical solutions of stochastic differential equations, and deep learning and machine learning applications in computational physics and mathematics. Key publications feature 'An adaptive multi-element generalized polynomial chaos method for stochastic differential equations' (2005, with G.E. Karniadakis), 'Multi-element generalized polynomial chaos for arbitrary probability measures' (2006, with G.E. Karniadakis), 'The multi-element probabilistic collocation method (ME-PCM): Error analysis and applications' (2008, with J. Foo and G.E. Karniadakis), 'DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations' (2023, with K. Tang and C. Yang), 'Adaptive deep density approximation for Fokker-Planck equations' (2022, Journal of Computational Physics, with K. Tang and Q. Liao), 'A minimum action method for dynamical systems with constant time delays' (2021, SIAM Journal on Scientific Computing, with J. Zhai), and 'A dynamic-solver-consistent minimum action method: With an application to 2D Navier-Stokes equations' (2017, Journal of Computational Physics, with H. Yu). He received the LSU Council on Research Summer Stipend Program award in 2010. His scholarly contributions have notably advanced methodologies in stochastic modeling and computational mathematics.