Advances in Model Reduction Methods
数学专题报告
报告题目(Title)：
Advances in Model Reduction Methods

报告人(Speaker)：
李秋齐（湖南大学）

地点(Place)：
后主楼 1223（腾讯会议：872-401-446）

时间(Time)：
2025年03月21日 周五 15:00-16:00

邀请人(Inviter)：
蔡永强
报告摘要
Model reduction methods are essential for simplifying complex systems and enabling efficient analysis of high-dimensional problems. This report provides a comprehensive overview of recent advances in model reduction techniques, such as reduced basis methods, low-rank representations (e.g., variable separation), data-driven approaches (including dynamic mode decomposition, operator inference, deep learning, and norm-optimal frameworks), and hybrid approaches. We highlight their theoretical foundations, algorithmic innovations.
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