桃子汉化组移植游戏大全

报告主题:  Reduced-order modeling for uncertainty quantification of parameterized fluid-structure interaction problems

报 告 人: 孙祥 副教授

报告时间：2025年 4月25日（星期五）上午10:00-11:00

报告地点：37号楼3A01

邀 请 人: 谷亚光 副教授

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数学桃子汉化组移植游戏大全

2025年4月21日

报告摘要：

A non-intrusive reduced-order modeling (ROM) method, based on tensor-train decomposition (TTD) and polynomial chaos expansion (PCE), is proposed for parameterized fluid-structure interaction problems. TTD is used to extract the spatial, temporal, and parametric modes into TT-cores to reduce the degrees of freedom. PCE is used to approximate the parameter-dependent TT-cores by utilizing a finite set of polynomials. To validate the proposed TTD-PCE, we considered 1D Burgers' and diffusion-reaction equations with random force terms. Compared to POD-PCE, TTD-PCE demonstrated superior performance, with eight times faster construction and two times faster prediction for a single sample. Moreover, a TTD-PCE-based uncertainty quantification (UQ) framework involving uncertainty estimation and sensitivity analysis is constructed. Subsequently, flow over a circular cylinder validated the effectiveness of the proposed method for FSI problems. Finally, a flexible filament with various conditions demonstrated the efficacy of the proposed method for UQ analysis. The results indicated a higher level of uncertainty at the free end of the self-propelled filament. Global sensitivity analysis revealed that the impact factor has different effects depending on the computational configurations. The unknown parameter of the filament was identified using TTD-PCE-based Bayesian inference, demonstrating TTD-PCE as a robust UQ framework for both calibration and parameter identification. The great potential of TTD-PCE in UQ analysis demonstrates it as a reliable tool for managing uncertainty in complex dynamical systems, providing valuable insight for inverse problems related to FSI problems.

报告人介绍：

孙祥，中国海洋大学副教授，硕士生导师。主要研究领域为模型降阶、不确定性量化以及机器学习。在JCP, JSC以及CiCP等计算数学高水平期刊上发表学术论文20余篇。现主持国家自然科学基金青年项目、山东省自然科学基金青年项目以及国家实验室科技创新项目等。