报告题目 (Title)：整合数据同化和机器学习的约束优化模型
报告人 (Speaker)： Hai Xiang Lin （林海翔） 教授（Delft University of Technology, Netherlands,荷兰代尔夫特理工大学）
报告时间 (Time)：2024年11月11日 (周一) 9:30-11:30
报告地点 (Place)：校本部F619
邀请人(Inviter)：白延琴 教授
主办部门：金沙威尼斯欢乐娱人城数学系
报告摘要：
Both data assimilation (DA) and machine learning (ML) techniques can be used to improve air quality forecast accuracy. DA is a model-based approach that reduces the uncertainty in the model using the information from observation data. At the same time, ML is a data-driven approach that tries to find the important features and their relations to the data without a mathematical-physical model, it tries to fit the data into some functional relationship through an optimization procedure. Physics-informed machine learning is a research field that is gaining increasing attention, where knowledge such as physical laws are used as constraints. Combining the power of the model-based DA method and the data-driven ML technique is the focus of much recent research, in this talk, we will discuss our experience of combining DA and ML through the case study in improving the accuracy of air quality forecast.
