报告题目：Reduced order modelling and its applications

报告时间：2024年4月9日 10:00-11:30

腾讯会议：542-331-431

邀请人：李义宝 教授

摘要：Reduced-order modelling (ROM) provides an economical way to construct low-dimensional parametric surrogates for rapid predictions of high-dimensional physical fields. This talk will present a physics-data combined machine learning (PDCML) method for non-intrusive ROM in small-data regimes. To overcome labelled data scarcity, a physics-data combined ROM framework is developed to jointly integrate the physical principle and the small labelled data into feedforward neural networks (FNN) via a step-by-step training scheme. This new PDCML method is tested on a series of nonlinear problems with different numbers of physical variables, and it is also compared with the data-driven ROM and the physics-guided ROM. The results demonstrate that the proposed method provides a cost-effective way for non-intrusive parametric ROM via machine learning, and it possesses good characteristics of high prediction accuracy, strong generalization capability and small data requirement. In this talk, a non-linear non-intrusive ROM based on Auto-encoder and self-attention will be also presented.

简介：肖敦辉，同济大学数学科学学院教授，国家高层次海外青年人才以及上海市高层次海外领军人才入选者，中国数学会计算数学分会第十一届常务理事， 张江数学研究院同济分院副院长。2016年获帝国理工流体力学博士学位，曾先后就职于英国帝国理工地球科学系和数据科学所，斯旺西大学辛克维奇工程计算中心。主持英国基金委如EPSRC， Royal Society和中国国家级项目多项，发表SCI论文40多篇。研究领域包括低阶计算模型、模型降阶、数据驱动模型、计算力学、物理与数据混合驱动计算模型、数据科学、计算流体力学和人工智能。