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 http://hyperion.usc.edu/MLUQ/agenda.html
Author: Jian-Xun Wang
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.
New Publication in Annals of Biomedical Engineering
J.-X. Wang, X. Hu, S. C. Shadden, Data-augmented modeling of intracranial pressure. Annals of Biomedical Engineering, 2018 (forthcoming)
See manuscript here [Arxiv]
Seminar Talk: “Data-enabled Computational Modeling”
ACMS Applied Math Seminar: Jian-Xun Wang
Thu Nov 29, 2018 3:30PM – 4:30PM
154 Hurley Hall
“Data-enabled Computational Modeling”
See flyer here
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.