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MEAM Seminar: “Data-Aware Computational Models for Science and Engineering”

February 14, 2023 at 10:00 AM - 11:30 AM

In the past half-century, partial differential equation (PDE)-based computational models have emerged as indispensable for science and engineering. However, remarkable gaps still exist between state-of-the-art simulations and reality, meaning that many simulations are ineffective in supporting decision-making or design under uncertainty for complex systems (e.g., Mars landing). To bridge the gap and fulfill challenging real-world missions, I develop data-aware computational models that combine increasingly available data with complex PDE-based models to make improved predictions, together with measures of their uncertainty.

In this talk, I will focus on two complementary approaches to data-aware computational modeling. First, I will discuss a new Bayesian method for updating/improving computational models and quantifying uncertainties based on measured data: our Kalman inversion approach is built on Kalman filtering and empirically converges in O(10) iterations with O(10) ensemble evaluations per iteration, enabling effective Bayesian calibration with very few model evaluations. Second, I will introduce our geometry-aware Fourier neural operator (FNO), a deep learning surrogate model which maps a given design geometry to a predicted PDE solution state. We use the automatic differentiation tools of deep learning packages to efficiently compute gradients of the FNO approximation, enabling real-time multiphysics engineering design optimization. These methods we developed have been successfully applied in complex applications ranging from Mars landing supersonic parachute, bacteria-resistant catheter design, the digital twin for airfoil damage detection, and the Earth system model for climate science.

Daniel Zhengyu Huang

Postdoctoral Scholar, Department of Computing & Mathematical Sciences and Department of Environmental Science & Engineering, California Institute of Technology

Daniel Zhengyu Huang is a postdoctoral researcher at the Computing + Mathematical Sciences Department and Environmental Science and Engineering Department at Caltech. He works at the interface of computational engineering and data science. He received his Ph.D. from Institute for Computational & Mathematical Engineering and Aeronautics and Astronautics Department at Stanford University in 2020. His doctoral dissertation won the Gene Golub Dissertation Award.


February 14, 2023
10:00 AM - 11:30 AM
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Mechanical Engineering and Applied Mechanics
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Wu and Chen Auditorium (Room 101), Levine Hall
3330 Walnut Street
Philadelphia, PA 19104 United States
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