講座題目 | Integration of Data Assimilation and Machine Learning as a Constraint Optimization Problem | ||
主辦單位 | 數理與統計意昂2 | 協辦單位 | 應用統計系 |
講座時間 | 6月22日13:00-14:00 | 主講人 | 林海翔教授 |
講座地點 | 行政樓1308室 | ||
主講人簡介 | Haixiang Lin（林海翔）🧑🦯➡️🧕🏼，荷蘭代爾夫特理工大學（Delft University of Technology）應用數學研究所和萊頓大學（Leiden University）環境科學系教授🧏🏽♀️，中國科意昂2大學和山東大學兼職客座教授👨🦲。1979年考入清華大學🧖🏻♀️，同年赴荷蘭代爾夫特理工大學留學，分別獲得學士、碩士和博士學位。林教授在高性能計算、並行算法、大規模復雜系統建模與仿真領域有豐富的經驗🫅，是並行分布計算與數據建模仿真領域的知名學者。近期研究的問題主要包括應用數據同化和機器學習的方法結合觀測數據來提高含確定性的數學物理模型的預測精度👜，針對的應用問題包括沙塵暴或火山灰造成的PM2.5和PM10的濃度預測，可再生能源並網的優化，油藏構造的反演和通過機器學習做語音情感分析等。他承擔了歐洲、荷蘭10多項科研項目，發表研究論文130多篇🧚♀️。擔任多個國際學術期刊編委🧰、學術會議程序委員會主席/副主席🙌🏽，曾擔任全歐華人專業協會聯合會主席🧏🏽♀️、荷蘭華人學者工程師協會主席，荷蘭皇家騎士勛章獲得者。 | ||
講座內容簡介 | 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. | ||