Speaker: Dunhui Xiao, professor,School of Mathematical Sciences,Tongji University

Date: December 12, 2023

Time: 15:00-16:00

Location: Tencent Meeting: 937 541 306

Sponsor: School of Mathematics, Shandong University

Abstract:

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.

For more information, please visit:

https://www.view.sdu.edu.cn/info/1020/186272.htm