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.