Event Description
Abstract:
Artificial Intelligence for Science (AI4Sci) has become a transformative approach in
modeling and understanding complex physical systems, encompassing different scales such as
atomistic systems and continuum systems. In atomistic systems, AI has shown potential in
accelerating simulations, optimizing molecular dynamics, and predicting material and
molecular properties through data-driven approaches, enhancing computational efficiency
while preserving accuracy. For continuum systems, AI provides powerful tools for solving
partial differential equations (PDEs) and learning physical patterns from data, capturing
intricate dynamics that govern physical and engineering processes. This work explores AI
methods--particularly equivariance for neural networks and neural operators--bridging
atomistic and continuum representations. We analyze the implications of incorporating
symmetries to improve model robustness and learning efficiency, providing a cohesive AI- driven framework for advancing scientific discovery. The findings aim to underscore the role
of AI in enhancing accuracy, applicability, scalability, interopretability, and generalization
across scales, from molecular simulations to physical modeling, opening pathways for next- generation applications in computational science.
Biography:
Wenhan Gao is a third-year Ph.D. student in Applied Mathematics at Stony Brook University, where he works under the supervision of Professor Yi Liu. Wenhan's research focuses on
equivariant neural networks, graph neural networks, and AI for partial differential equations. Wenhan's work seeks to leverage the power of symmetries to aid AI models, particularly in
fields such as computer vision (image and video generation), physical simulation (modeling
climate change), and computational chemistry (drug discovery). He has published papers on
the aforementioned topics in leading venues like NeurIPS, Transactions on Machine Learning
Research (TMLR), and Journal of Computational Physics (JCP). He also has several preprints
under review in leading venues like ICLR and CVPR. In addition to his research, Wenhan has
served as a reviewer for top-tier conferences, including ICLR, NeurIPS, ICML, and KDD, and
as a lecturer for undergraduate and graduate courses at Stony Brook University. Wenhan was
awarded the NeurIPS Travel Award and Excellence in Teaching for Fall 2023.
