CRE Contributions at WCCM-ECCOMAS 2026 in Munich

From 19 to 24 July 2026, the 17th World Congress on Computational Mechanics and the 10th European Congress on Computational Methods in Applied Sciences and Engineering were jointly held as WCCM-ECCOMAS 2026 in Munich. The international congress brought together researchers from academia and industry to exchange recent developments in computational mechanics and computational methods in engineering and science.

Research from the Chair for Reliability Engineering (CRE) was represented through contributions by Weiming Zheng and Ali Kilicsoy.

Weiming Zheng's Research:

Hierarchical Surrogate Modeling and Uncertainty Quantification for Aluminum Profile Process Chain

Abstract:
Due to the widespread use of aluminum profiles and the presence of unavoidable process and material uncertainties, quantification of these uncertainties across the complete process chains is essential for reliably controlling the critical-to-quality (CTQ) properties, e.g. wall thickness and springback, of the final products. Such process chains consist of multiple interconnected subsystems linked by so-called linking variables, which serve simultaneously as outputs of upstream subsystems and inputs to downstream subsystems. This coupling poses significant challenges to conventional forward uncertainty propagation methods as their research subjects are mostly individual components or simple sequential systems. In this work, an efficient hierarchical surrogate modeling framework with active learning is proposed to enable forward uncertainty propagation throughout critical process units of a typical aluminum profile process chain consisting of hot extrusion, straightening, bending and heat treatment. Field-type linking variables, e.g. stress field and temperature field, are considered. Epistemic uncertainty is decomposed into upstream-propagated uncertainty associated with linking variables and prediction uncertainty related to surrogate model accuracy of the current subsystem, and a new acquisition function is formulated to identify the most informative sampling points. Aleatory uncertainty is fully characterized through probabilistic descriptions of system inputs, which are determined based on prior knowledge, experimental observations, and Bayesian updating. Uncertainty propagation is then efficiently performed using deterministic surrogate models trained on high-fidelity finite element (FEM) simulations. The proposed framework enables accurate and computationally efficient uncertainty propagation across coupled process units, thereby laying a solid foundation for future robustness optimization and sensitivity analysis.

Ali Kilicsoy's Research:

Failure Probability Estimation via Bayesian Last Layer

Abstract:
Reliability analysis for systems fundamentally requires estimation of its probability of failure, which is a multi-dimensional integral. In such complex systems, evaluating via computationally heavy models through direct Monte-Carlo can be arduous, as failure points become rarer. This hurdle is alleviated by use of surrogate models approximating the given system response, subsequently enabling more efficient inference. Nevertheless, the application of surrogate models is constrained by the dimensionality of the reliability problem. With this work, we seek to estimate failure probabilities of intermediate- to high-dimensional uncertain systems (in the order of several tens of random input variables) and tackle the curse of dimensionality. Hereby we setup the Bayesian Last Layer model as a surrogate, more specifically, we use the constrained, empirical Bayes version. The surrogate is initially constructed using a small training set and then refined by expansion of the training set by use of an active learning strategy. These new data points are selected by a large inference set through the current surrogate model based on failure-relevant criteria. The results are compared against well-established surrogate modelling techniques currently wide-spread in reliability analysis. This comparison ranges from sample efficiency, robustness to increased dimensionality and accuracy of failure probability estimation. The goal is to assess the effectiveness of the surrogate model relative to established surrogates and emphasize its advantages and limitations for high-dimensional reliability problems.

The two contributions reflect CRE’s ongoing research in uncertainty quantification, surrogate modelling and advanced methods for reliability analysis.

Further information: WCCM-ECCOMAS 2026 – Official Website