Matthias G. R. Faes

According to our database, Matthias G. R. Faes authored at least 14 papers between 2016 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

On fractional moment estimation from polynomial chaos expansion.

Lukás Novák, Marcos A. Valdebenito, Matthias G. R. Faes

Reliab. Eng. Syst. Saf., 2025

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., March, 2024

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

2023

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

2022

Parallel adaptive Bayesian quadrature for rare event estimation.

Chao Dang, Pengfei Wei, Matthias G. R. Faes, Marcos A. Valdebenito, Michael Beer

Reliab. Eng. Syst. Saf., 2022

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

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

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

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

Augmented reliability analysis for estimating imprecise first excursion probabilities in stochastic linear dynamics.

Matthias G. R. Faes, Marcos A. Valdebenito, Xiukai Yuan, Pengfei Wei, Michael Beer

Adv. Eng. Softw., 2021

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

A fast inverse approach for the quantification of set-theoretical uncertainty.

Lars Bogaerts, Matthias G. R. Faes, David Moens

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2019

2017

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

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

2016

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