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ORCIDMatthias G. R. Faes
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2020 – today
- 2025
[j11]Lukás Novák, Marcos A. Valdebenito
, Matthias G. R. Faes:
On fractional moment estimation from polynomial chaos expansion. Reliab. Eng. Syst. Saf. 254: 110594 (2025)- 2024
- [j10]A. O. M. Kilicsoy, J. Liedmann, Marcos A. Valdebenito, F.-J. Barthold, Matthias G. R. Faes:
Sobolev Neural Network With Residual Weighting as a Surrogate in Linear and Non-Linear Mechanics. IEEE Access 12: 137144-137161 (2024) - [j9]Xiukai Yuan, Weiming Zheng, Chaofan Zhao, Marcos A. Valdebenito, Matthias G. R. Faes, Yiwei Dong:
Line sampling for time-variant failure probability estimation using an adaptive combination approach. Reliab. Eng. Syst. Saf. 243: 109885 (2024) - [i3]Lukás Novák, Marcos A. Valdebenito, Matthias G. R. Faes:
On Fractional Moment Estimation from Polynomial Chaos Expansion. CoRR abs/2403.01948 (2024) - [i2]A. O. M. Kilicsoy, J. Liedmann, Marcos A. Valdebenito, F.-J. Barthold, Matthias G. R. Faes:
Sobolev neural network with residual weighting as a surrogate in linear and non-linear mechanics. CoRR abs/2407.16466 (2024) - 2023
- [j8]Xiukai Yuan, Yugeng Qian, Jingqiang Chen, Matthias G. R. Faes, Marcos A. Valdebenito, Michael Beer:
Global failure probability function estimation based on an adaptive strategy and combination algorithm. Reliab. Eng. Syst. Saf. 231: 108937 (2023) - 2022
- [j7]Chao Dang, Pengfei Wei, Matthias G. R. Faes, Marcos A. Valdebenito, Michael Beer:
Parallel adaptive Bayesian quadrature for rare event estimation. Reliab. Eng. Syst. Saf. 225: 108621 (2022) - 2021
- [j6]Matthias G. R. Faes, Marcos A. Valdebenito, Xiukai Yuan, Pengfei Wei, Michael Beer:
Augmented reliability analysis for estimating imprecise first excursion probabilities in stochastic linear dynamics. Adv. Eng. Softw. 155: 102993 (2021) - [j5]Xiukai Yuan, Shaolong Liu, Marcos A. Valdebenito, Matthias G. R. Faes, Danko J. Jerez, Hector A. Jensen, Michael Beer:
Decoupled reliability-based optimization using Markov chain Monte Carlo in augmented space. Adv. Eng. Softw. 157-158: 103020 (2021) - [j4]Lechang Yang, Pidong Wang, Wenhua Zhao, Chenxing Wang, Xiuli Wu, Matthias G. R. Faes:
On investigation of the Bayesian anomaly in multiple imprecise dependent information aggregation for system reliability evaluation. Int. J. Intell. Syst. 36(6): 2895-2921 (2021) - [j3]Xiukai Yuan, Matthias G. R. Faes, Shaolong Liu, Marcos A. Valdebenito, Michael Beer:
Efficient imprecise reliability analysis using the Augmented Space Integral. Reliab. Eng. Syst. Saf. 210: 107477 (2021) - [j2]Laura Bartsoen, Matthias G. R. Faes, Mariska Wesseling, Roel Wirix-Speetjens, David Moens, Ilse Jonkers, Jos Vander Sloten:
Computationally Efficient Optimization Method to Quantify the Required Surgical Accuracy for a Ligament Balanced TKA. IEEE Trans. Biomed. Eng. 68(11): 3273-3280 (2021)
2010 – 2019
- 2019
- [j1]Matthias G. R. Faes, Ghosh Dastidar Sabyasachi, David Moens:
Hybrid spatial uncertainty analysis for the estimation of imprecise failure probabilities in Laser Sintered PA-12 parts. Comput. Math. Appl. 78(7): 2395-2406 (2019) - [c2]Lars Bogaerts, Matthias G. R. Faes, David Moens:
A fast inverse approach for the quantification of set-theoretical uncertainty. SSCI 2019: 768-775 - 2017
- [c1]Matteo Broggi, Matthias G. R. Faes, Edoardo Patelli, Yves Govers, David Moens, Michael Beer:
Comparison of Bayesian and interval uncertainty quantification: Application to the AIRMOD test structure. SSCI 2017: 1-8 - 2016
- [i1]Matthias G. R. Faes, Wim Abbeloos, Frederik Vogeler, Hans Valkenaers, Kurt Coppens, Toon Goedemé, Eleonora Ferraris:
Process Monitoring of Extrusion Based 3D Printing via Laser Scanning. CoRR abs/1612.02219 (2016)
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