Peimeng Yin

Assistant Professor   Department of Mathematical Sciences   The University of Texas at El Paso   Email: pyin@utep.edu    (No longer use yinp@ornl.gov !!!)                 URL: Github Homepage   URL: ORNL Homepage   Google Scholar   ResearchGate   ORCID       CyMail Google       Gmail Baidu       Outlook Library       Office Lib       Overleaf Citrix       Cybox UCAMS       Okta Tokens   MathSciNet   Python Software CFD-Lab   Wolfram Matlab

Biography

Dr. Peimeng Yin is an Assistant Professor in the Department of Mathematical Sciences at the University of Texas at El Paso (UTEP). He received his Ph.D. degree in the Department of Mathematics from Iowa State University in 2019 under the supervision of Prof. Hailiang Liu. Prior to joining UTEP, Dr. Yin held research positions at several institutions, including Oak Ridge National Laboratory (ORNL) as a Postdoctoral Research Associate in the Computer Science and Mathematics Division, the University of Kansas Medical Center as a Research Assistant Professor (research track) in Radiation Oncology, and Wayne State University as a Postdoctoral Fellow in Mathematics.

His research lies in the area of Computational Mathematics and Applied Mathematics, with a primary focus on Numerical Analysis, Partial Differential Equations, Scientific Computing, and Data Science. He tackles modern challenges in PDEs, including high-order operators, multiscale dynamics, singular sources, and high-dimensional problems. He is passionate about developing computational tools that are both mathematically rigorous and capable of advancing scientific discovery. His work emphasizes the analysis and development of efficient, structure-preserving numerical methods for PDEs, with particular focus on finite element and discontinuous Galerkin methods, energy-stable time integration, dynamical low-rank approximations, and neural network-enhanced solvers.

Research Interests

• Numerical analysis, PDEs, scientific computing, and data science

• Finite element and discontinuous Galerkin methods

• Maximum-principle-preserving numerical methods

• Dynamical low rank approximation

• Stable and structure-preserving methods for gradient flows

• Computationally efficient methods for multiscale and high-dimensional PDEs

• Theory and numerical methods for PDEs with singularities

• Mathematical theory of deep learning and PDE-based data-driven modeling

Selected recent work

• Hailiang Liu, Zhongming Wang and Peimeng Yin*. A positivity-preserving hybrid DDG method for Poisson--Nernst--Planck systems. Journal of Computational Physics, 546:114508, 2026. [PDF] [DOI] [arXiv]

• Huini Liu, Nianyu Yi and Peimeng Yin*. A structure-preserving relaxation Crank-Nicolson finite element method for the Schrödinger-Poisson equation. IMA Journal of Numerical Analysis, DOI:https://doi.org/10.1093/imanum/draf117, 2026. [PDF] [DOI] [arXiv]

• Peimeng Yin*, Eirik Endeve, Cory D. Hauck and Stefan R. Schnake. Towards dynamical low-rank approximation for neutrino kinetic equations. Part I: Analysis of an idealized relaxation model. Mathematics of Computation, 94:1199-1233, 2025. [PDF] [DOI] [arXiv]

• Jiaxiong Hao, Yunqing Huang, Nianyu Yi and Peimeng Yin*. Neural network-enhanced hr-adaptive finite element algorithm for parabolic equations. arXiv preprint, arXiv:2503.12717, 2025. [arXiv]

• Jun Yang, Nianyu Yi and Peimeng Yin*. A second-order dynamical low-rank mass-lumped finite element method for the Allen-Cahn equation. BIT Numerical Mathematics, 66:6, 2026. [PDF] [DOI] [arXiv]

• Hengguang Li, Peimeng Yin* and Zhimin Zhang. A $C^0$ finite element method for the biharmonic problem with Navier boundary conditions in a polygonal domain. IMA Journal of Numerical Analysis, 43:1779-1801, 2023. [PDF] [DOI] [arXiv]

• Hailiang Liu, Zhongming Wang, Peimeng Yin and Hui Yu. Positivity-preserving third order DG schemes for Poisson--Nernst--Planck equations. Journal of Computational Physics, 452:110777, 2022. [PDF] [DOI]

Available RA Positions

Dr. Peimeng Yin, from the Department of Mathematical Sciences at the University of Texas at El Paso, is seeking doctoral graduate students with focus on applied math, numerical analysis, and scientific computing. If you are interested, please contact Dr. Yin via pyin@utep.edu.