Xiaoliang Wan

Xiaoliang Wan is a Professor in the Department of Mathematics at Louisiana State University (LSU), with an affiliation at the Center for Computation and Technology (CCT). His research focuses on Scientific Machine Learning, Stochastic Modeling, Numerical Methods for Partial Differential Equations (PDEs), and the Minimum Action Method for Large Deviation Principles. He has developed software tools such as the Multi-Element Probabilistic Collocation Method (ME-PCM) and the hp-adaptive Minimum Action Method for computational physics and uncertainty quantification.

Wan has taught numerous advanced courses, including Numerical Analysis, Numerical Linear Algebra, and Stochastic PDEs. His work bridges computational mathematics with applications in fluid dynamics, oceanography, and climate modeling. Key contributions include integrating machine learning with traditional numerical methods to solve high-dimensional PDEs and stochastic systems.

His research emphasizes adaptive algorithms, rare event simulation, and parameterization techniques for complex systems. Recent work includes applications of deep learning to ocean turbulence modeling and the development of hybrid FEM-PINN methods for time-dependent PDEs.