工程可靠性与随机力学国际联合研究中心

2026 年第 8 期（总第 124 期）学术报告

工程力学研究中心第 79 期学术报告

文远讲坛 354 期

报告主题
TOPIC

不确定线性动力系统首次超越可靠性的主动学习与高级仿真

Active Learning and Advanced Simulation for First-Excursion
Reliability of Uncertain Linear Dynamical Systems

报告人
SPEAKER

Dr. Marcos Valdebenito

Senior Scientist, TU Dortmund University, Germany

报告时间
TIME

2026 年 6 月 30 日（周二）上午 10:00-11:00

报告地点
VENUE

同济大学土木大楼 A305

主持人
CHAIR

彭勇波 教授

联系人: 牛立志

报告内容
Abstract

First-excursion probabilities constitute a fundamental measure for quantifying the reliability of engineering systems subjected to random dynamic loading. For linear dynamical systems with deterministic structural properties and Gaussian stochastic excitation, a variety of efficient simulation methods have been developed and successfully applied. In many practical applications, however, structural parameters such as masses, stiffnesses, or damping coefficients are themselves uncertain. The resulting reliability problem involves both excitation uncertainty and parameter uncertainty, leading to a substantial increase in computational complexity.

This presentation introduces a simulation framework for the efficient estimation of first-excursion probabilities of linear dynamical systems with uncertain structural parameters. The proposed approach combines active-learning Gaussian process regression, importance sampling, and multidomain Line Sampling. A surrogate model is constructed in the space of uncertain structural parameters and is employed to identify regions that contribute most significantly to failure. Active learning is used to adaptively refine the surrogate only where required. The resulting model is subsequently used to construct an importance sampling density for the structural parameters, while multidomain Line Sampling is employed to efficiently account for stochastic excitation.

The proposed methodology preserves the accuracy of simulation-based reliability analysis while substantially reducing the associated computational effort. Numerical examples involving stochastic structural dynamics are used to demonstrate the performance of the approach and to illustrate the benefits of combining surrogate modelling, active learning, and advanced simulation techniques for rare-event estimation.

报告人简介
Speaker Bio

Dr. Marcos Valdebenito is Senior Scientist at the Chair for Reliability Engineering at TU Dortmund University, Germany. He obtained his doctoral degree in Civil Engineering from University of Innsbruck, Austria, after completing his engineering and master’s studies at Santa Maria University, Chile. His research focuses on uncertainty quantification, structural reliability, stochastic dynamics, Bayesian updating, surrogate modelling, and reliability-based design optimization in computational mechanics. He has authored more than 100 journal publications. Dr. Valdebenito received the K.J. Bathe Award in 2016 and a research fellowship from the Alexander von Humboldt Foundation. He currently serves on the editorial boards of the international journals Computers & Structures, Structural Safety, and Machine Learning for Computational Science and Engineering.