Recent progress on data driven reduced order models for kinetic transport problems
Yingda Cheng (email@example.com)
In recent years, data driven reduced order models (ROMs) have become a major tool in tackling high dimensional kinetic transport models. Based on ROM techniques including POD, reduced basis and machine learning, surrogate models are constructed capturing the essential behavior of the models, accelerating computational tasks such as uncertainty quantification and inverse problem and controls. Innovative techniques including nonlinear ROM, structure preserving turns out essential for building ROMs for multi scale kinetic problems. This mini symposium aims to bring together experts in ROM and kinetic simulations to discuss recent progress and outlook potential future research in this area.