Yiwei Wang
Assistant Professor
Department of Computer Science
University of California at Merced
Dr. Wang’s research lies at the intersection of variational modeling, scientific computing, and machine learning, with applications in physics, materials science, biology, and data science. He is interested in developing multiscale, thermodynamically consistent, variational models and structure-preserving numerical methods for complex dissipative systems, such as liquid crystals, polymeric fluids, reaction-diffusion processes, and complex systems in biology. His recent work explores how machine learning can enhance variational modeling and computation, by learning unknown dynamics, building coarse-grained closures, and solving high-dimensional PDE models. He also explores variational perspectives on machine learning, by interpreting learning dynamics as dissipative systems governed by energy-dissipation laws and designing machine learning algorithms inspired by structure-preserving numerical discretization.