Matthias Faes

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Professor (full)

Technische Universität Dortmund

Germany

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About

Professor Matthias Faes is a full professor at Technische Universität Dortmund in Germany. His research focuses on areas such as reliability analysis, stochastic dynamics, and uncertainty quantification, as outlined in his recent articles. Notable topics include Bayesian methods, probability estimation, and optimization methodologies within engineering systems. His contributions aim to advance the understanding and assessment of complex systems under uncertainty.

Recent Grants

Grant: Close

Generalised inverse uncertainty quantification in numerical models

Open Date: 2018-10-01

Close Date: 2021-10-01

Grant: Close

Fused Deposition Modeling voor Betrouwbare Functionele Kunststofcomponenten met beperkte Anisotropie

Open Date: 2013-10-01

Close Date: 2017-10-01

Articles (20)

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2022

Collaborators (6)

Michael Beer

GERMANY

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Yue HU

Leibniz University Hannover

GERMANY

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Ilse Jonkers

Katholieke Universiteit Leuven

BELGIUM

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Marcos Valdebenito

Chief Engineer

Technische Universität Dortmund

GERMANY

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Andreas Menzel

Technische Universität Dortmund

GERMANY

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Nikos Lagaros

Professor of Structural Optimization

National Technical University of Athens

GREECE

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Matthias Faes

Research Interests

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About

Recent Grants

Articles (20)

Probability Sensitivity Estimation with Respect to Distribution Parameters via the Method of Moments

A Bayesian piecewise fitting method for estimating probability distributions of performance functions

Time-dependent reliability analysis by a single-loop Bayesian active learning method using Gaussian process regression

First excursion probability sensitivity in stochastic linear dynamics by means of Domain Decomposition Method

Avoiding two common pitfalls in uncertainty propagation

Interval Fields for Geotechnical Engineering Uncertainty Analysis under Limited Data

An Importance Sampling Approach for Estimating First Excursion Probabilities of Linear Systems Subject to Non-Gaussian Loading

A Data Driven Black Box Approach for the Inverse Quantification of Set-Theoretical Uncertainty

Transient dynamic robust topology optimization methodology for continuum structure under stochastic uncertainties

Special Section on Reliability Modeling and Assessment of Complex Engineering Systems with Mixed Uncertainty

A topology-based in-plane filtering technique for the combined topology and discrete fiber orientation optimization

Collaborative and Adaptive Bayesian Optimization for bounding variances and probabilities under hybrid uncertainties

Effect of uncertainty of material parameters on stress triaxiality and Lode angle in finite elasto-plasticity—A variance-based global sensitivity analysis

No-Free-Lunch Theorems for Reliability Analysis

Estimation of an imprecise power spectral density function with optimised bounds from scarce data for epistemic uncertainty quantification

Global failure probability function estimation based on an adaptive strategy and combination algorithm

First-passage probability estimation of high-dimensional nonlinear stochastic dynamic systems by a fractional moments-based mixture distribution approach

Application of interval field method to the stability analysis of slopes in presence of uncertainties

Bayesian parameter estimation of ligament properties based on tibio-femoral kinematics during squatting

Parallel adaptive Bayesian quadrature for rare event estimation

Collaborators (6)

Michael Beer

Yue HU

Ilse Jonkers

Marcos Valdebenito

Andreas Menzel

Nikos Lagaros

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