Speaker: Prof. Xiaofei Guan, Tongji University
Time: 10:30-11:30 , Nov.11
Location: SIST 1C 101
Host: Prof. Qifeng Liao
Abstract:
Multiscale modeling for predicting heat conduction behavior of heterogeneous solids with uncertain material parameters remains a challenging problem. This is mainly due to the intrinsic low dimensional representation of high-dimensional stochastic material parameters, and the fact that many repeated evaluations of the multiscale model are often required. To overcome the current limitations, we propose an innovative multi-modes based constraint energy minimizing generalized multiscale finite element method (MCEM-GMsFEM), which can transform the original stochastic multiscale model with high-dimensional uncertain material parameters into a series of recursive multiscale models sharing the same deterministic material parameters. Then, a preconditioning conjugate gradient based on incomplete LU decomposition (PCG-ILU) is designed to effectively reduce the complexity of repeated computation of discretized multiscale systems. Furthermore, the convergence analysis is established, and the optimal error estimates are derived for the multi-modal multiscale method. Finally, some numerical examples are given to validate the theoretical results. The results indicate that the multi-modes multiscale model is a robust integrated method with the excellent performance, which can efficiently and accurately estimate the heat transfer response of heterogeneous solids with high-dimensional uncertain material parameters, and can significantly reduce the computational time.
Bio:
Xiaofei Guan is an associate professor and doctoral supervisor of the school of Mathematical Sciences of Tongji University. Xiaofei Guan is a standing member of the uncertainty quantification special committee of China Society of industrial and applied mathematics, member of the uncertain system analysis and simulation Professional Committee of China simulation society, innovation and entrepreneurship tutor for college students. His research interests include data-driven multi-scale multi physical field coupling modeling and deep learning. Xiaofei Guan received PhD in Academy of Mathematics and Systems Science, Chinese Academy of Sciences , he did postdoctoral research in the Department of structure and mechanics of Vienna university of technology(2012.6-2012.10).He was a visiting scholar in the Department of mathematics at the University of California San Diego (2013.12-2015.1). Xiaofei Guan presided over and participated in a number of national and ministerial scientific research projects, and published more than 20 high-level academic papers in internationally renowned journals.
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