Dr. Jian-Xun Wang

Recent advances in data science techniques, combined with the everincreasing availability of high-fidelity simulation/measurement data open up new opportunities for developing data-enabled computational modeling of fluid systems. However, compared to most general data science applications, the cost of data acquisition for modeling complex physical/ physiological systems is usually expensive or even prohibitive, which poses challenges for directly leveraging the success of existing deep learning models. On the other hand, there is often a richness of prior knowledge, including physical laws and phenomenological principles, which can be leveraged to enable efficient learning in the “small data” regime. This talk will focus on physics-informed deep learning (PIDL), which has recently attracted increasing attention in the scientific machine learning community. The objective is to enable effective learning in a datascarce setting by incorporating physics knowledge (e.g., conservation laws) to inform the learning architecture construction and/or constrain the training process. Novel physics-informed learning frameworks will be discussed, which enable us to solve forward and inverse problems in a unified manner, where sparse data can be naturally assimilated. Our recent developments in PIDL for, e.g., surrogate modeling, superresolution, inverse modeling, and uncertainty quantification, will be presented. The effectiveness of the proposed methods will be demonstrated on a number of fluid problems that are relevant to hemodynamic applications.

Dr. Jian-Xun Wang is an assistant professor of Aerospace and Mechanical Engineering at the University of Notre Dame. Dr. Wang received a Ph.D. in Aerospace Engineering from Virginia Tech in 2017 and was a postdoctoral scholar at the University of California, Berkeley before joining Notre Dame in 2018. His research focuses on developing data-driven/data -augmented computational modeling, which broadly revolves around physics-informed machine learning, Bayesian data assimilation, and uncertainty quantification. His current research interests involve surrogate modeling for fluid flows based on physics-constrained deep learning, dataaugmented physiological model based on Bayesian data assimilation (e.g., assimilation of 4D flow MRI in hemodynamic modeling and dataaugmented intracranial modeling).