Presented PCML paper in USC Workshop on Research Challenges at the interface of Machine Learning and Uncertainty Quantification

Presented a paper entitled of Surrogate Modeling for Fluid Flows Based on Physics-Constrained, Label-Free Deep Learning at USC Workshop on Research Challenges at the interface of Machine Learning and Uncertainty Quantification. Please check out

Invited talk at Workshop of Machine Learning for Computational Fluid and Solid Dynamics

Tuesday, February 19, 2019 – Thursday, February 21, 2019  [www] Santa Fe

Recent breakthroughs in machine learning (ML), including the stunning successes of AlphaZero and AlphaGo, have demonstrated tremendous potential for transforming scientific computing.  The application of these exciting advances in algorithms and computer architectures to the computational modeling and simulation community introduces several additional requirements and challenges beyond traditional applications such as data analytics and computer vision. These include physical constraints (the subject of CNLS Physics-Informed Machine Learning workshops in 2016 and 2018), the need for uncertainty quantification (UQ), and computational requirements for embedded ML models, e.g. for parameter tuning, sub-scale physics models, optimization, UQ, or data assimilation. This workshop will bring together international leaders in the development and application of ML methods for fluid and solid dynamics.

Welcome to Jian-Xun Wang’s Research Group

Dr. Wang’s current research focuses on data-enabled, physics-based computational modeling for a number of physical systems, including cardiovascular/cerebrovascular flows, intracranial system, turbulent flows, and other computational-mechanics problems. The main idea is to develop accurate physics-based computational models by leveraging available data from high-fidelity simulations, experiments, and clinical measurements using advanced data assimilation and machine learning techniques. Moreover, he is also interested in quantifying and reducing uncertainties associated with the developed computational models.