Huan Lei

Assistant Professor, Department of Computational Mathematics, Science and Engineering

Assistant Professor, Department of Statistics & Probability

Location: 1501 Engineering Bldg

Phone: 517-884-8947

Email: leihuan@msu.edu

Website: https://leihuan-mp.github.io/

Photo of: Huan Lei

Bio

My research interests lie in applied mathematics and scientific computing, in particular, multi-scale modeling with applications to fluid physics, materials science, and biophysics. The research goal is to establish accurate modeling of multi-scale systems relevant to meso-scale transport, non-Newtonian hydrodynamics, and kinetic transport processes arising from various science and engineering applications. For such problems, the multi-scale and high-dimensional nature imposes a fundamental challenge; empirical models based on ad-hoc closure assumptions often show limitations. Currently, my group is devoted to constructing machine-learning based models for such systems directly from the first-principle-based descriptions. In particular, we focus on developing numerical methods to learn reliable and numerically-stable models that faithfully entail the micro-scale interactions, retain physical interpretation, and respect physical constraints. The long-term goal is to achieve predictive modeling of these multi-scale systems beyond phenomenologically understanding and establish integrated control across multiple scales.
###Education
Ph. D. 2012, Applied Mathematics, Brown University - Advisor: George Karniadakis
B.S. 2005, Special Class for the Gifted Young, Univ. of Science & Technology of China
###Professional Appointment
Post-doctoral Associate, Brown University (2012 -2013)
Post-doctoral Associate, Pacific Northwest National Laboratory (2013 -2015)
Scientist, Pacific Northwest National Laboratory (2015 -2019)
###Research Interests
Multiscale modeling, Stochastic modeling, Scientific machine-learning, Fluid physics
###Openings
My group is recruiting Ph.D. students, Postdocs, and Visiting students; [more info](https://leihuan-mp.github.io/position/). If you are interested, please contact me by email.

Courses

CMSE 890: Selected Topics in CMSE

Selected Publications

W. E, H. Lei, P. Xie, and L. Zhang. Machine learning-assisted multi-scale modeling. *Journal of Mathematical Physics*, 64(7):071101, 2023 View Publication
H. Lei, L. Wu, and W. E. Machine Learning Based non-Newtonian Fluid Model with Molecular Fidelity. *Phys. Rev. E* 102: 043309, 2020 View Publication
H. Lei, N. A. Baker, and X. Li. Data-Driven Parameterization of the Generalized Langevin Equation. *Proc. Natl. Acad. Sci.* 113 (50):14183–14188, 2016 View Publication
L. Fang , P. Ge, L. Zhang, W. E, and H. Lei. DeePN<sup>2</sup>: A Deep Learning-Based non-Newtonian Hydrodynamic Model. *Journal of Machine Learning* 1: 114–140, 2022 View Publication
L. Lyu and H. Lei. Construction of coarse-grained molecular dynamics with many-body non-Markovian memory. *Phys. Rev. Lett.* 131:177301, 2023 View Publication
Y. Zhu, H. Lei, and C. Kim. General validity of the second fluctuation-dissipation theorem in the non-equilibrium steady state: Theory and applications. *Physica Scripta*, 98(11):115402, 2023 View Publication
Z. She, P. Ge, and H. Lei. Data-driven construction of stochastic reduced dynamics encoded with non-Markovian features. *J. Chem. Phys.* 158:034102, 2023 View Publication