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Wednesday, February 3, 2021 – 12:00PM to 1:00PM
Title: "Where are we with data-driven surrogate modeling for various physical simulations?"
Virtual Seminar: https://duke.zoom.us/j/9088262425
A surrogate model is built to accelerate computationally expensive physical simulations, which is useful in multi-query problems, such as inverse problem, uncertainty quantification, design optimization, and optimal control. In this talk, two types of data-driven surrogate modeling techniques will be discussed, i.e., the black-box approach that incorporates only data and the physics-informed approach that incorporates the physics information as well as data within the surrogate models. The advantages and disadvantages of each method will be discussed. Furthermore, several recent developments at LLNL of data-driven physics-informed surrogate modeling techniques will be introduced in the context of various physical simulations.