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Huan Lei's research focuses on computational mathematics, particularly the development of numerical methods for learning partial differential equation (PDE) and stochastic differential equation (SDE) models arising from multi-scale problems. He is particularly interested in designing structure-preserving algorithms that retain essential mathematical properties, conservation laws, variational structures, and physical constraints. A central aspect of his work is integrating scientific machine learning (SciML) with numerical analysis to construct accurate and physically interpretable PDE and SDE models that can be applied to multi-scale systems. He emphasizes the limitations of conventional approaches and aims to develop mathematically rigorous and computationally efficient methods that enhance the simulation of complex dynamical systems.
Brown University •
Pacific Northwest National Laboratory •
Pacific Northwest National Laboratory •
Department of Psychology