Matthias Faes

发表

Robust efficient global optimisation via adaptive surrogate refinement

David Moens, Noémie Le Carrer, Matthias Faes, 2019 .

On the Robust Estimation of Small Failure Probabilities for Strong Nonlinear Models

Jonathan Sadeghi, Marco de Angelis, Edoardo Patelli, 2019, ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg.

Inverse quantification of epistemic uncertainty under scarce data: Bayesian or Interval approach?

Edoardo Patelli, John E. Mottershead, David Moens, 2019 .

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

David Moens, Matthias Faes, Lars Bogaerts, 2019, 2019 IEEE Symposium Series on Computational Intelligence (SSCI).

Hybrid spatial uncertainty analysis for the estimation of imprecise failure probabilities in Laser Sintered PA-12 parts

David Moens, Matthias Faes, Ghosh Dastidar Sabyasachi, 2019, Comput. Math. Appl..

Operator norm theory as an efficient tool to propagate hybrid uncertainties and calculate imprecise probabilities

Michael Beer, David Moens, Marcos A. Valdebenito, 2021 .

Additive Manufacturing as a key enabler for light weight design

David Moens, Matthias Faes, 2015 .

Process Monitoring of Extrusion Based 3D Printing via Laser Scanning pdf

Toon Goedemé, Frederik Vogeler, Hans Valkenaers, 2014, ArXiv.

Adaptive sparse grid approximation for high dimensional interval field construction

David Moens, Matthias Faes, Robin Callens, 2020 .

Identification and quantification of multivariate possibilistic manufacturing uncertainty in dynamic numerical models

David Moens, Dirk Vandepitte, Matthias Faes, 2017 .

IMPRECISE RANDOM FIELD ANALYSIS FOR TRANSIENT DYNAMICS

David Moens, Matthias Faes, 2019 .

A MACHINE LEARNING APPROACH FOR THE INVERSE QUANTIFICATION OF SET-THEORETICAL UNCERTAINTY

David Moens, Matthias Faes, Lars Bogaerts, 2019 .

Robust uncertainty quantification in structural dynamics under scarse experimental modal data: A Bayesian-interval approach

David Moens, Dirk Vandepitte, Matthias Faes, 2020 .

Recent Trends in the Modeling and Quantification of Non-probabilistic Uncertainty

David Moens, Matthias Faes, Matthias G. R. Faes, 2019, Archives of Computational Methods in Engineering.

Comparison between Bayesian and intervaluncertainty quantification: application to theAIRMOD test structure

Edoardo Patelli, David Moens, Michael Beer, 2017 .

Imprecise random field analysis with parametrized kernel functions

David Moens, Matthias Faes, D. Moens, 2019 .

Efficient imprecise reliability analysis using the Augmented Space Integral

Shaolong Liu, Michael Beer, Xiukai Yuan, 2021, Reliab. Eng. Syst. Saf..

A Review of Interval Field Approaches for Uncertainty Quantification in Numerical Models

David Moens, Dirk Vandepitte, Matthias Faes, 2021 .

An efficient importance sampling approach for reliability analysis of time-variant structures subject to time-dependent stochastic load

Shaolong Liu, Michael Beer, Xiukai Yuan, 2021 .

Quantification of non-homogeneous interval uncertainty based on scatter in modal properties

David Moens, Matthias Faes, 2017 .

Extrusion-based additive manufacturing of ZrO2 using photoinitiated polymerization

Frederik Vogeler, Eleonora Ferraris, Matthias Faes, 2016 .

Interval methods for the identification and quantification of inhomogeneous uncertainty in Finite Element models

Matthias Faes, Matthias G. R. Faes, 2017 .

Comparison of Bayesian and interval uncertainty quantification: Application to the AIRMOD test structure

Edoardo Patelli, David Moens, Matteo Broggi, 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

Influence of Inter-layer Cooling time on the Quasi-static Properties of ABS Components Produced via Fused Deposition Modelling

David Moens, Eleonora Ferraris, Matthias Faes, 2016 .

Identification of Interval fields for spatial uncertainty representation in Finite Element models

David Moens, Dirk Vandepitte, Matthias Faes, 2016 .