Prof. Dr. Matthias Faes

Profil

Derzeitige StellungProfessor W-3 und Äquivalente
FachgebietAngewandte Mechanik, Statik und Dynamik,Konstruktiver Ingenieurbau, Bauinformatik und Baubetrieb,Mechanik
KeywordsRisk & Reliability, Spectral uncertainty analysis, Imprecise probability theory, Interval analysis
Auszeichnungen

2023: EASD Junior Research Prize in the Area of Development of Methodologies for Structural Dynamics. European Association of Structural Dynamics.

2021: Top Cited Article 2020-2021 in the "International Journal for Numerical Methods in Engineering"

2020: Best student paper award (as co-author and mentor of the first author) at APSSRA 2020 in Tokyo, Japan.

2020: Willy Asselman Foundation - Willy Asselman Onderzoeksaward.

2019: ISIPTA - IJAR Young researcher award for research excellence in imprecise probabilities

2018: ECCOMAS Award for the best Ph.D. theses in 2017 on computational methods in applied sciences and engineering in Europe

2018: 2nd Laureate BNCTAM Award for the Best PhD Thesis of 2017 in applied or theoretical mechanics in Belgium

2016: Excellent paper award at ISEM18, awarded by the International Academy for Production Engineering.

Aktuelle Kontaktadresse

LandDeutschland
OrtDortmund
Universität/InstitutionTechnische Universität Dortmund
Institut/AbteilungFakultät Maschinenbau

Gastgeber*innen während der Förderung

Prof. Dr.-Ing. Michael BeerInstitut für Risiko und Zuverlässigkeit, Gottfried Wilhelm Leibniz Universität Hannover, Hannover
Beginn der ersten Förderung01.08.2020

Programm(e)

2019Humboldt-Forschungsstipendien-Programm für Postdocs

Publikationen (Auswahl)

2023Bartsoen, Laura and Faes, Matthias G.R. and Andersen, Michael Skipper and Wirix-Speetjens, Roel and Moens, David and Jonkers, Ilse and Sloten, Jos Vander: Bayesian parameter estimation of ligament properties based on tibio-femoral kinematics during squatting. In: Mechanical Systems and Signal Processing, 182, 2023, 109525
2022Ypsilantis, Konstantinos-Iason and Faes, Matthias G.R. and Ivens, Jan and Lagaros, Nikos D. and Moens, David: An approach for the concurrent homogenization-based microstructure type and topology optimization problem. In: Computers \& Structures, 272, 2022, 106859
2022Yang, Lechang and Bi, Sifeng and Faes, Matthias G.R. and Broggi, Matteo and Beer, Michael: Bayesian inversion for imprecise probabilistic models using a novel entropy-based uncertainty quantification metric. In: Mechanical Systems and Signal Processing, 162, 2022, 107954
2022Dang, Chao and Wei, Pengfei and Faes, Matthias G.R. and Beer, Michael: Bayesian probabilistic propagation of hybrid uncertainties: {Estimation} of response expectation function, its variable importance and bounds. In: Computers \& Structures, 270, 2022, 106860
2022Song, Jingwen and Wei, Pengfei and Valdebenito, Marcos A. and Faes, Matthias and Beer, Michael: Data-driven and active learning of variance-based sensitivity indices with {Bayesian} probabilistic integration. In: Mechanical Systems and Signal Processing, 163, 2022, 108106
2022Faes, Matthias G.R. and Broggi, Matteo and Chen, Guan and Phoon, Kok-Kwang and Beer, Michael: Distribution-free {P}-box processes based on translation theory: {Definition} and simulation. In: Probabilistic Engineering Mechanics, 69, 2022, 103287
2022Faes, Matthias G.R. and Broggi, Matteo and Spanos, Pol D. and Beer, Michael: Elucidating appealing features of differentiable auto-correlation functions: {A} study on the modified exponential kernel. In: Probabilistic Engineering Mechanics, 69, 2022, 103269
2022Wang, Gengxiang and Faes, Matthias G.R. and Cheng, Fuan and Shi, Tengfei and Gao, Peng: Extension of dashpot model with elastoplastic deformation and rough surface in impact behavior. In: Chaos, Solitons \& Fractals, 162, 2022, 112402
2022van Mierlo, Conradus and Burmberger, Lukas and Daub, Marco and Duddeck, Fabian and Faes, Matthias G.R. and Moens, David: Interval methods for lack-of-knowledge uncertainty in crash analysis. In: Mechanical Systems and Signal Processing, 168, 2022, 108574
2022Dang, Chao and Wei, Pengfei and Faes, Matthias G.R. and Valdebenito, Marcos A. and Beer, Michael: Interval uncertainty propagation by a parallel {Bayesian} global optimization method. In: Applied Mathematical Modelling, 108, 2022, 220--235
2022Ni, Peihua and Jerez, Danko J. and Fragkoulis, Vasileios C. and Faes, Matthias G. R. and Valdebenito, Marcos A. and Beer, Michael: Operator {Norm}-{Based} {Statistical} {Linearization} to {Bound} the {First} {Excursion} {Probability} of {Nonlinear} {Structures} {Subjected} to {Imprecise} {Stochastic} {Loading}. In: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 8, 2022, 04021086
2022Dang, Chao and Wei, Pengfei and Faes, Matthias G.R. and Valdebenito, Marcos A. and Beer, Michael: Parallel adaptive {Bayesian} quadrature for rare event estimation. In: Reliability Engineering \& System Safety, 225, 2022, 108621
2022Dang, Chao and Valdebenito, Marcos A. and Faes, Matthias G.R. and Wei, Pengfei and Beer, Michael: Structural reliability analysis: {A} {Bayesian} perspective. In: Structural Safety, 99, 2022, 102259
2022Zhao, Yanlin and Yang, Jianhong and Faes, Matthias G.R. and Bi, Sifeng and Wang, Yao: The sub-interval similarity: {A} general uncertainty quantification metric for both stochastic and interval model updating. In: Mechanical Systems and Signal Processing, 178, 2022, 109319
2022Callens, Robin R.P. and Faess, Matthias G.R. and Moens, David: {MULTILEVEL} {QUASI}-{MONTE} {CARLO} {FOR} {INTERVAL} {ANALYSIS}. In: International Journal for Uncertainty Quantification, 12, 2022, 1--19
2021Yuan, Xiukai and Liu, Shaolong and Faes, Matthias and Valdebenito, Marcos.A. and Beer, Michael: An efficient importance sampling approach for reliability analysis of time-variant structures subject to time-dependent stochastic load. In: Mechanical Systems and Signal Processing, 159, 2021, 107699
2021Faes, Matthias G.R. and Valdebenito, Marcos A. and Yuan, Xiukai and Wei, Pengfei and Beer, Michael: Augmented reliability analysis for estimating imprecise first excursion probabilities in stochastic linear dynamics. In: Advances in Engineering Software, 155, 2021, 102993
2021Bartsoen, Laura and Faes, Matthias G.R. and Wesseling, Mariska and Wirix-Speetjens, Roel and Moens, David and Jonkers, Ilse and Sloten, Jos Vander: Computationally {Efficient} {Optimization} {Method} to {Quantify} the {Required} {Surgical} {Accuracy} for a {Ligament} {Balanced} {TKA}. In: IEEE Transactions on Biomedical Engineering, 68, 2021, 3273--3280
2021Yuan, Xiukai and Liu, Shaolong and Valdebenito, Marcos A. and Faes, Matthias G.R. and Jerez, Danko J. and Jensen, Hector A. and Beer, Michael: Decoupled reliability-based optimization using {Markov} chain {Monte} {Carlo} in augmented space. In: Advances in Engineering Software, 157-158, 2021, 103020
2021Yuan, Xiukai and Faes, Matthias G.R. and Liu, Shaolong and Valdebenito, Marcos A. and Beer, Michael: Efficient imprecise reliability analysis using the {Augmented} {Space} {Integral}. In: Reliability Engineering \& System Safety, 210, 2021, 107477
2021Faes, Matthias G.R. and Daub, Marco and Marelli, Stefano and Patelli, Edoardo and Beer, Michael: Engineering analysis with probability boxes: {A} review on computational methods. In: Structural Safety, 93, 2021, 102092
2021Faes, Matthias G.R. and Valdebenito, Marcos A.: Fully decoupled reliability-based optimization of linear structures subject to {Gaussian} dynamic loading considering discrete design variables. In: Mechanical Systems and Signal Processing, 156, 2021, 107616
2021Dannert, Mona M. and Faes, Matthias G.R. and Fleury, Rodolfo M.N. and Fau, Amelie and Nackenhorst, Udo and Moens, David: Imprecise random field analysis for non-linear concrete damage analysis. In: Mechanical Systems and Signal Processing, 150, 2021, 107343
2021van Mierlo, Conradus and Faes, Matthias G.R. and Moens, David: Inhomogeneous interval fields based on scaled inverse distance weighting interpolation. In: Computer Methods in Applied Mechanics and Engineering, 373, 2021, 113542
2021Callens, Robin R.P. and Faes, Matthias G.R. and Moens, David: Local explicit interval fields for non-stationary uncertainty modelling in finite element models. In: Computer Methods in Applied Mechanics and Engineering, 379, 2021, 113735
2021Yang, Lechang and Wang, Pidong and Zhao, Wenhua and Wang, Chenxing and Wu, Xiuli and Faes, Matthias G. R.: On investigation of the {Bayesian} anomaly in multiple imprecise dependent information aggregation for system reliability evaluation. In: International Journal of Intelligent Systems, 36, 2021, 2895--2921
2021Faes, Matthias G.R. and Valdebenito, Marcos A. and Moens, David and Beer, Michael: Operator norm theory as an efficient tool to propagate hybrid uncertainties and calculate imprecise probabilities. In: Mechanical Systems and Signal Processing, 152, 2021, 107482
2021Faes, Matthias G. R. and Moens, David and Beer, Michael and Zhang, Hao and Phoon, Kok-Kwang: Special {Section}: {Nonprobabilistic} and {Hybrid} {Approaches} for {Uncertainty} {Quantification} and {Reliability} {Analysis}. In: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, 7, 2021, 020301
2020Faes, Matthias G.R. and Valdebenito, Marcos A. and Moens, David and Beer, Michael: Bounding the first excursion probability of linear structures subjected to imprecise stochastic loading. In: Computers \& Structures, 239, 2020, 106320
2020Faes, Matthias G.R. and Valdebenito, Marcos A.: Fully decoupled reliability-based design optimization of structural systems subject to uncertain loads. In: Computer Methods in Applied Mechanics and Engineering, 371, 2020, 113313
2020Faes, Matthias and Moens, David: On auto‐ and cross‐interdependence in interval field finite element analysis. In: International Journal for Numerical Methods in Engineering, 121, 2020, 2033--2050
2020Faes, Matthias and Moens, David: Recent {Trends} in the {Modeling} and {Quantification} of {Non}-probabilistic {Uncertainty}. In: Archives of Computational Methods in Engineering, 27, 2020, 633--671
2020Imholz, Maurice and Faes, Matthias and Vandepitte, Dirk and Moens, David: Robust uncertainty quantification in structural dynamics under scarse experimental modal data: {A} {Bayesian}-interval approach. In: Journal of Sound and Vibration, 467, 2020, 114983
2020Chen, Zhao-Yue and Imholz, Maurice and Li, Liu and Faes, Matthias and Moens, David: Transient landing dynamics analysis for a lunar lander with random and interval fields. In: Applied Mathematical Modelling, 88, 2020, 827--851
2019Faes, Matthias and Broggi, Matteo and Patelli, Edoardo and Govers, Yves and Mottershead, John and Beer, Michael and Moens, David: A multivariate interval approach for inverse uncertainty quantification with limited experimental data. In: Mechanical Systems and Signal Processing, 118, 2019, 534--548
2019Faes, Matthias and Sabyasachi, Ghosh Dastidar and Moens, David: Hybrid spatial uncertainty analysis for the estimation of imprecise failure probabilities in {Laser} {Sintered} {PA}-12 parts. In: Computers \& Mathematics with Applications, 78, 2019, 2395--2406
2019Faes, Matthias and Moens, David: Imprecise random field analysis with parametrized kernel functions. In: Mechanical Systems and Signal Processing, 134, 2019, 106334
2019Faes, Matthias and Moens, David: Multivariate dependent interval finite element analysis via convex hull pair constructions and the {Extended} {Transformation} {Method}. In: Computer Methods in Applied Mechanics and Engineering, 347, 2019, 85--102
2019Faes, Matthias and Sadeghi, Jonathan and Broggi, Matteo and de Angelis, Marco and Patelli, Edoardo and Beer, Michael and Moens, David: On the {Robust} {Estimation} of {Small} {Failure} {Probabilities} for {Strong} {Nonlinear} {Models}. In: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, 5, 2019, 041007
2019Le Carrer, Noémie and Moens, David and Faes, Matthias: Robust efficient global optimisation via adaptive surrogate refinement. In: PAMM, 19, 2019,
2018Faes, Matthias and Moens, David: Inverse {Interval} {Field} {Quantification} via {Digital} {Image} {Correlation}. In: Applied Mechanics and Materials, 885, 2018, 304--310
2017Faes, M. and Cerneels, J. and Vandepitte, D. and Moens, D.: Identification and quantification of multivariate interval uncertainty in finite element models. In: Computer Methods in Applied Mechanics and Engineering, 315, 2017, 896--920
2017Faes, M. and Moens, D.: Identification and quantification of spatial interval uncertainty in numerical models. In: Computers \& Structures, 192, 2017, 16--33
2017Pavan, M. and Faes, M. and Strobbe, D. and Van Hooreweder, B. and Craeghs, T. and Moens, D. and Dewulf, W.: On the influence of inter-layer time and energy density on selected critical-to-quality properties of {PA12} parts produced via laser sintering. In: Polymer Testing, 61, 2017, 386--395
2017Faes, M. and Wang, Y. and Lava, P. and Moens, D.: Variability, heterogeneity, and anisotropy in the quasi-static response of laser sintered {PA12} components: {Variability}, {Heterogeneity} and {Anisotropy} in mechanical properties of {LS} - {PA12}. In: Strain, 53, 2017, e12219
2016Faes, M. and Vleugels, J. and Vogeler, F. and Ferraris, E.: Extrusion-based additive manufacturing of {ZrO2} using photoinitiated polymerization. In: CIRP Journal of Manufacturing Science and Technology, 14, 2016, 28--34
2016Faes, Matthias and Cerneels, Jasper and Vandepitte, Dirk and Moens, David: Influence of measurement data metrics on the identification of {Interval} {Fields} for the representation of spatial variability in finite element models: {Influence} of measurement data metrics on the identification of {Interval} {Fields} for the representation of spatial variability in finite element models. In: PAMM, 16, 2016, 27--30