题目：Predicting Turbulent Systems from Limited Measurements: Classical Methods to Machine Learning

时间：2023年6月20日 9:00-11:00

地点：bat365在线中国官网登录入口 F301会议室

报告人：Dr. Vikrant Gupta（南方科技大学）

邀请人：刘应征 教授（叶轮机械研究所）

Biography

Dr. Vikrant Gupta is currently a Research Associate Professor at the Southern University of Science & Technology where he applies data-driven and dynamical systems tools to study complex flows. The areas of application include wall turbulence, wind and tidal energy, and low-emissions gas turbines. He has published 24 SCI papers in high-impact-factor journals, including 10 in Journal of Fluid Mechanics. He has also been awarded two NSFC grants for his research on wall turbulence and wind energy.

Abstract

This seminar will analyse three main predictive methods: linearised (low-rank approximation) models, data assimilation, and model-free neural networks. On the one hand, linearised models require little measurement data but necessitate deep understanding of system dynamics. On the other hand, model-free networks require high-resolution space-time measurement data but require minimal knowledge of system dynamics. This seminar will demonstrate that the measurement conditions required for effective application of data-driven methods, whether model-free or model-based, are closely related to complexity measures from chaos theory. These findings can guide the systematic collection of data and selection of predictive methods for turbulence forecasting in practical systems.