I am a postdoctoral fellow at Institute for Foundations of Data Science (FDS), Yale University. I obtained my PhD degree in Applied Mathematics and Computational Science at University of Pennsylvania in 2023, advised by Prof. Paris Perdikaris. Before that, I received my B.S. degree in Mathematics at Wuhan University. My research interests lie in the intersection of machine learning, scientific computing, and computational physics. I am particularly interested in developing scalable and robust algorithms for solving partial differential equations, and leveraging these algorithms to solve challenging problems in science and engineering.

News

• [09/2024] Our paper “Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials” is now available on arXiv.

• [05/2024] Our paper “Bridging Operator Learning and Conditioned Neural Fields: A Unifying Perspective” is now available on arXiv.

• [02/2024] Our new paper “PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks” is now available on arXiv.

• [01/2024] Our paper “Respecting causality for training physics-informed neural networks” has been accepted to CMAME.