Process Monitoring of Extrusion Based 3D Printing via Laser Scanning. - Matthias G. R. Faes; Wim Abbeloos; Frederik Vogeler; Hans Valkenaers; Kurt Coppens; Toon Goedemé; Eleonora Ferraris (CoRR, 2016)

Comparison of Bayesian and interval uncertainty quantification: Application to the AIRMOD test structure. - Matteo Broggi; Matthias G. R. Faes; Edoardo Patelli; Yves Govers; David Moens; Michael Beer (SSCI, 2017)

A fast inverse approach for the quantification of set-theoretical uncertainty. - Lars Bogaerts; Matthias G. R. Faes; David Moens (SSCI, 2019)

Hybrid spatial uncertainty analysis for the estimation of imprecise failure probabilities in Laser Sintered PA-12 parts. - Matthias G. R. Faes; Ghosh Dastidar Sabyasachi; David Moens (Comput. Math. Appl., 2019)

Augmented reliability analysis for estimating imprecise first excursion probabilities in stochastic linear dynamics. - Matthias G. R. Faes; Marcos A. Valdebenito; Xiukai Yuan; Pengfei Wei 002; Michael Beer (Adv. Eng. Softw., 2021)

Decoupled reliability-based optimization using Markov chain Monte Carlo in augmented space. - Xiukai Yuan; Shaolong Liu; Marcos A. Valdebenito; Matthias G. R. Faes; Danko J. Jerez; Hector A. Jensen; Michael Beer (Adv. Eng. Softw., 2021)

On investigation of the Bayesian anomaly in multiple imprecise dependent information aggregation for system reliability evaluation. - Lechang Yang; Pidong Wang; Wenhua Zhao; Chenxing Wang; Xiuli Wu; Matthias G. R. Faes (Int. J. Intell. Syst., 2021)

Efficient imprecise reliability analysis using the Augmented Space Integral. - Xiukai Yuan; Matthias G. R. Faes; Shaolong Liu; Marcos A. Valdebenito; Michael Beer (Reliab. Eng. Syst. Saf., 2021)

Computationally Efficient Optimization Method to Quantify the Required Surgical Accuracy for a Ligament Balanced TKA. - Laura Bartsoen; Matthias G. R. Faes; Mariska Wesseling; Roel Wirix-Speetjens; David Moens; Ilse Jonkers; Jos Vander Sloten (IEEE Trans. Biomed. Eng., 2021)

Parallel adaptive Bayesian quadrature for rare event estimation. - Chao Dang; Pengfei Wei 002; Matthias G. R. Faes; Marcos A. Valdebenito; Michael Beer (Reliab. Eng. Syst. Saf., 2022)

Global failure probability function estimation based on an adaptive strategy and combination algorithm. - Xiukai Yuan; Yugeng Qian; Jingqiang Chen; Matthias G. R. Faes; Marcos A. Valdebenito; Michael Beer (Reliab. Eng. Syst. Saf., 2023)

Sobolev Neural Network With Residual Weighting as a Surrogate in Linear and Non-Linear Mechanics. - A. O. M. Kilicsoy; J. Liedmann; Marcos A. Valdebenito; F.-J. Barthold; Matthias G. R. Faes (IEEE Access, 2024)

Line sampling for time-variant failure probability estimation using an adaptive combination approach. - Xiukai Yuan; Weiming Zheng; Chaofan Zhao; Marcos A. Valdebenito; Matthias G. R. Faes; Yiwei Dong (Reliab. Eng. Syst. Saf., 2024)

On fractional moment estimation from polynomial chaos expansion. - Lukás Novák; Marcos A. Valdebenito; Matthias G. R. Faes (Reliab. Eng. Syst. Saf., 2025)

Asymptotic subset simulation: An efficient extrapolation tool for small probabilities approximation. - Mohsen Rashki; Matthias G. R. Faes; Pengfei Wei 002; Jingwen Song (Reliab. Eng. Syst. Saf., 2025)

A Bayesian piecewise fitting method for estimating probability distributions of performance functions. - Yan-Gang Zhao; Ya-Ting Liu; Pei-Pei Li; Ye-Yao Weng; Marcos A. Valdebenito; Matthias G. R. Faes (Reliab. Eng. Syst. Saf., 2025)

Efficient forward and inverse uncertainty quantification for dynamical systems based on dimension reduction and Kriging surrogate modeling in functional space. - Zhouzhou Song; Weiyun Xu; Marcos A. Valdebenito; Matthias G. R. Faes (CoRR, 2025)