My interest lies in data-driven physical problems, from the approach of applied math and also machine learning. Applied math provides us with well-explained modeling for complicated physical problems but its computability is limited by troubles like the curse of dimensionality, and numerical stability. Machine learning has the potential to bridge this gap, although the practical optimization stage could be quite a black box. I look forward to integrating these two fields elegantly so that we can still have a well-explained model as well as a scalable solver for the physical problem in interest.
Thomas Y. Hou, Shumao Zhang (alphabetical order)
Thomas Y. Hou, Shumao Zhang (alphabetical order)
Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference, ICML, 2021
Shumao Zhang, Thomas Y. Hou, Pengchuan Zhang
Solving Bayesian Inverse Problems from the Perspective of Deep Generative Networks, Computational Mechanics, 2019
Thomas Y. Hou, Ka Chun Lam, Pengchuan Zhang, Shumao Zhang (alphabetical order)