工程可靠性与随机力学国际联合研究中心

2025年第5期（总第97期）学术报告

工程力学研究中心第51期学术报告

文远讲坛320期

报告主题

TOPIC

高效鲁棒可靠性分析：非完备信息下的决策制定

Efficient robust reliability analysis: decision making under incomplete knowledge  

报告人

SPEAKER

Prof. Matthias Faes

德国多特蒙德大学教授，可靠性工程研究所所长，国际期刊

Reliab Eng Syst Saf与Mech Syst Signal Pr副主编

报告时间

TIME

2025年7月9日（周三）下午15:30-16:30

报告地点

VENUE

同济大学土木大楼 A305

主持人

CHAIR

陈建兵教授、彭勇波教授联系人：律梦泽博士

报告摘要

Abstract

Numerical tools to approximate the solution of (sets of) differential equations have become indispensable in the design of engineering components from the micro-scale to complete structures. Thanks to these tools, an engineer is now able to design, test and optimize designs long before a first prototype is built. However, despite the highly detailed numerical predictions that can be obtained, the results of these calculations often show a non-negligible discrepancy with the actual physical behavior of the structure. At the core of this discrepancy lies uncertainty in the description of the model physics, as well as the governing parameters.

Uncertainties are for instance commonly encountered in the context of structural dynamics, where for instance the effect natural phenomena such as earthquakes or wind loads on structures has to be considered. Indeed, due to the sheer complexity of the underlying physics, the corresponding dynamical loads that act on the system often cannot be described in a crisp way. Stochastic processes provide a rigorous framework to deal with the uncertainties and space/time correlations of uncertain loads by resorting to the well-documented framework of probability theory. However, in practice, the analyst is often confronted with limited, incomplete or conflicting sources of data (i.e., epistemic uncertainty) due to limitations in time, budget and/or measurement resolution. In this case, there might be simply not enough information to warrant the computational cost of performing extremely detailed reliability analyses with very small failure probabilities. This begs the question: where should we invest our resources – in making our reliability analyses more accurate or in making them more robust?

In this talk, I will present some philosophical, theoretical and very practical numerical calculation schemes that are aimed at contributing to answering this question. The presentation will draw from recent work we performed in the field of reliability sensitivity and imprecise reliability analysis, highlighting recent advances in surrogate modelling (functional dimensionality reduction, Bayesian active learning, polynomial chaos expansions), decoupling schemes (operator norm theory) and efficient sampling schemes (multi-domain line sampling, directional importance sampling).

报告人简介

Speaker Bio

Matthias Faes became a full Professor in Reliability Engineering at TU Dortmund at the age of 30, since February 2022. Before, he was a post-doctoral fellow of the Research Foundation Flanders (FWO) working at the Department of Mechanical Engineering of KU Leuven and was also affiliated to the Institute for Risk and Reliability at the University of Hannover as an Alexander von Humboldt Fellow. He graduated summa cum laude as Master of Science in Engineering Technology in 2013 and obtained his PhD in Engineering Technology from KU Leuven in 2017. Since then, he works on theoretical and numerical methods to perform efficient uncertainty quantification and reliability analysis under scarce data and information, including inverse and data-driven methods, surrogate modeling schemes, stochastic fields and imprecise probabilities.

Matthias Faes is a Laureate of the 2017 PhD award of the Belgian National Committee for Applied and Theoretical Mechanics, winner of the 2017 ECCOMAS European PhD award for best PhD thesis in 2017 on computational methods in applied sciences and engineering in Europe, winner of the 2019 ISIPTA - IJAR Young Researcher Award for outstanding contributions to research on imprecise probabilities and the 2023 EASD Junior Research Prize for his contribution to the development of methodologies for structural dynamics, among other awards. He is Associate Editor at Mechanical Systems and Signal Processing, and Associate Managing Editor of the ASCE-ASME Journal of Risk and Uncertainty in Engineering system parts A and B, among other journals. Matthias Faes is author of more than 90 journal papers and more than 90 conference contributions and he has a Google Scholar H-index of 26 (2400+ citations) since 2016.