Category: Seminar

Department Seminar at IUPUI (Virtual)

Posted on by Jian-Xun Wang

Speaker: Jian-Xun Wang, Date / Time: Nov. 12, 2020, 11:00 – 12:00 pm, Location: (Virtual)

Abstract

Recent advances in data science techniques, combined with the ever-increasing availability of high-fidelity simulation/measurement data open up new opportunities for developing data-enabled computational modeling of fluid systems. However, compared to most computer 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 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 data-scarce setting by incorporating physics knowledge (e.g., conservation laws) to inform the learning architecture construction and/or constrain the training process. Our recent developments in PIDL for, e.g., surrogate modeling, super-resolution, 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. 

Department Seminar Talk at CU Boulder, Mar 4th. 2020,

Posted on by Jian-Xun Wang

https://www.colorado.edu/center/aerospacemechanics/events/spring-2020-fluids-structure-materials-fsm-seminars

Speaker: Jian-Xun Wang, Date / Time: Wednesday, March 4, 2020, 4:30 – 5:30 pm, Location: AERO 114

Abstract:

Recent advances in data science techniques, combined with the ever-increasing availability of high-fidelity simulation/measurement data open up new opportunities for developing data-enabled computational modeling of fluid systems. However, compared to most applications in the computer science community, 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 usually richness of prior knowledge including physical laws and phenomenological theories, which can be leveraged to enable efficient learning in the “small data” regime. This talk will focus on the data-driven/data-augmented modeling for fluid flows based using physics-constrained machine learning and Bayesian data assimilation techniques, where both the sparse data and physical principles are leveraged. Specifically, several separate but related topics will be covered, including machine learning assisted RANS turbulence modeling, physics-constrained deep learning for fluid surrogate modeling and super-resolution, and multi-fidelity Bayesian data assimilation for field inversion in fluid simulations.

Several presentations at APS DFD 2019, Seattle.

Posted on by Jian-Xun Wang

J Wang’s group will give three presentations at 72nd APS-DFD in Seattle,

See you in Seattle!

 

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

Posted on by Jian-Xun Wang

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

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

Posted on by Jian-Xun Wang

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.