معرفی
Peimeng Yin is an Assistant Professor in the Department of Mathematical Sciences at the University of Texas at El Paso (UTEP). His research focuses on computational mathematics and applied mathematics, emphasizing numerical analysis, partial differential equations, scientific computing, and data science. He designs and implements numerical algorithms for PDEs with applications in physics, astrophysics, engineering, biology, energy, and oncology. Key interests include discontinuous Galerkin methods, structure-preserving numerical techniques, dynamical low-rank approximation, and PDE-based data-driven modeling.
His work bridges rigorous mathematical theory with practical computation, addressing challenges like high derivatives, singularities, and multiscale phenomena. Recent research involves adaptive finite element methods, positivity-preserving schemes, and machine learning-enhanced algorithms for parabolic equations. Yin's studies often emphasize energy stability and error estimation in gradient flow systems, with applications ranging from fluid dynamics to biomedical decision support systems.
Notable contributions include advancements in biharmonic problem solvers, neutrino kinetic equations, and surgical outcome prediction models. He maintains an active publication record in top-tier computational mathematics journals, with a focus on developing robust numerical frameworks for complex physical systems.
