Matthias G.R. Faes

2.6k total citations
131 papers, 1.9k citations indexed

About

Matthias G.R. Faes is a scholar working on Statistics, Probability and Uncertainty, Civil and Structural Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Matthias G.R. Faes has authored 131 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 95 papers in Statistics, Probability and Uncertainty, 61 papers in Civil and Structural Engineering and 25 papers in Computational Theory and Mathematics. Recurrent topics in Matthias G.R. Faes's work include Probabilistic and Robust Engineering Design (94 papers), Structural Health Monitoring Techniques (40 papers) and Advanced Multi-Objective Optimization Algorithms (23 papers). Matthias G.R. Faes is often cited by papers focused on Probabilistic and Robust Engineering Design (94 papers), Structural Health Monitoring Techniques (40 papers) and Advanced Multi-Objective Optimization Algorithms (23 papers). Matthias G.R. Faes collaborates with scholars based in Germany, Belgium and China. Matthias G.R. Faes's co-authors include David Moens, Michael Beer, Marcos A. Valdebenito, Eleonora Ferraris, Chao Dang, Pengfei Wei, Jef Vleugels, Matteo Broggi, Xiukai Yuan and Edoardo Patelli and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Computer Methods in Applied Mechanics and Engineering.

In The Last Decade

Matthias G.R. Faes

118 papers receiving 1.8k citations

Peers

Countries citing papers authored by Matthias G.R. Faes

Since Specialization

Citations

This map shows the geographic impact of Matthias G.R. Faes's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthias G.R. Faes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthias G.R. Faes more than expected).

Fields of papers citing papers by Matthias G.R. Faes

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthias G.R. Faes. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthias G.R. Faes. The network helps show where Matthias G.R. Faes may publish in the future.

Co-authorship network of co-authors of Matthias G.R. Faes

This figure shows the co-authorship network connecting the top 25 collaborators of Matthias G.R. Faes. A scholar is included among the top collaborators of Matthias G.R. Faes based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matthias G.R. Faes. Matthias G.R. Faes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Aleatory and epistemic uncertainty in reliability analysis: An engineering perspective 2025 ·Structural Safety ·(unknown), Marcos A. Valdebenito, Chao Dang, Michael Beer, Matthias G.R. Faes	0
2	Time-dependent structural reliability analysis: A single-loop approximate Bayesian active learning quadrature approach 2025 ·Mechanical Systems and Signal Processing ·Chao Dang, (unknown), Marcos A. Valdebenito, Matthias G.R. Faes	1
3	First excursion probability sensitivity in stochastic linear dynamics by means of Domain Decomposition Method 2025 ·Mechanical Systems and Signal Processing ·(unknown), Marcos A. Valdebenito, Matthias G.R. Faes	0
4	Reliability analysis combining method of moments with control variates 2025 ·Probabilistic Engineering Mechanics ·(unknown), Xuan‐Yi Zhang, Marcos A. Valdebenito, Matthias G.R. Faes	3
5	First excursion probability of dynamical systems: A review on computational methods 2025 ·Mechanical Systems and Signal Processing ·(unknown), (unknown), Michael Beer, Matthias G.R. Faes, Costas Papadimitriou, N. Zhou	4
6	Transient dynamic robust topology optimization methodology for continuum structure under stochastic uncertainties 2025 ·Computer Methods in Applied Mechanics and Engineering ·Zeng Meng, (unknown), Yongxin Gao, Matthias G.R. Faes, Quhao Li	1
7	On fractional moment estimation from polynomial chaos expansion 2024 ·Reliability Engineering & System Safety ·(unknown), Marcos A. Valdebenito, Matthias G.R. Faes	5
8	Confined seepage analysis of saturated soils using fuzzy fields 2024 ·Journal of Rock Mechanics and Geotechnical Engineering ·(unknown), Kok‐Kwang Phoon, Yong Liu, Marcos A. Valdebenito, Matthias G.R. Faes	1
9	An approximate decoupled reliability-based design optimization method for efficient design exploration of linear structures under random loads 2024 ·Computer Methods in Applied Mechanics and Engineering ·(unknown), (unknown), (unknown), Marcos A. Valdebenito, Matthias G.R. Faes, Jianbing Chen	4
10	Structural reliability analysis using imprecise evolutionary power spectral density functions 2024 ·Journal of Physics Conference Series ·(unknown), Chao Dang, Matthias G.R. Faes, Marcos A. Valdebenito, Michael Beer	1
11	Directional importance sampling for dynamic reliability of linear structures under non-Gaussian white noise excitation 2024 ·Mechanical Systems and Signal Processing ·Xuan‐Yi Zhang, (unknown), Marcos A. Valdebenito, Matthias G.R. Faes	2
12	Soft Monte Carlo Simulation for imprecise probability estimation: A dimension reduction-based approach 2023 ·Structural Safety ·Azam Abdollahi, (unknown), Matthias G.R. Faes, Mohsen Rashki	7
13	Combining data and physical models for probabilistic analysis: A Bayesian Augmented Space Learning perspective 2023 ·Probabilistic Engineering Mechanics ·(unknown), Pengfei Wei, Jingwen Song, Matthias G.R. Faes, Marcos A. Valdebenito, Michael Beer	2
14	Efficient quantification of composite spatial variability: A multiscale framework that captures intercorrelation 2023 ·Composite Structures ·(unknown), (unknown), Matthias G.R. Faes, Dirk Vandepitte, David Moens	10
15	Operator norm-based determination of failure probability of nonlinear oscillators with fractional derivative elements subject to imprecise stationary Gaussian loads 2023 ·Mechanical Systems and Signal Processing ·(unknown), Vasileios C. Fragkoulis, (unknown), (unknown), Marcos A. Valdebenito, Matthias G.R. Faes, Michael Beer	12
16	Structural reliability analysis by line sampling: A Bayesian active learning treatment 2023 ·Structural Safety ·Chao Dang, Marcos A. Valdebenito, Matthias G.R. Faes, Jingwen Song, Pengfei Wei, Michael Beer	28
17	Efficient decoupling approach for reliability-based optimization based on augmented Line Sampling and combination algorithm 2023 ·Computers & Structures ·Xiukai Yuan, Marcos A. Valdebenito, Baoqiang Zhang, Matthias G.R. Faes, Michael Beer	18
18	Decoupled reliability-based optimization using Markov chain Monte Carlo in augmented space 2021 ·Advances in Engineering Software ·Xiukai Yuan, (unknown), Marcos A. Valdebenito, Matthias G.R. Faes, (unknown), H.A. Jensen, Michael Beer	21
19	Operator Norm-Based Statistical Linearization to Bound the First Excursion Probability of Nonlinear Structures Subjected to Imprecise Stochastic Loading 2021 ·ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering ·(unknown), (unknown), Vasileios C. Fragkoulis, Matthias G.R. Faes, Marcos A. Valdebenito, Michael Beer	10
20	Inverse quantification of epistemic uncertainty under scarce data: Bayesian or Interval approach? 2019 ·elib (German Aerospace Center) ·Matthias G.R. Faes, Matteo Broggi, Edoardo Patelli, Yves Govers, John E. Mottershead, Michael Beer, David Moens	3

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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