Hussein Rappel
Hussein Rappel
The Alan Turing institute
Verified email at
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Cited by
A tutorial on Bayesian inference to identify material parameters in solid mechanics
H Rappel, LAA Beex, JS Hale, L Noels, SPA Bordas
Archives of Computational Methods in Engineering 27, 361-385, 2020
Identifying elastoplastic parameters with Bayes’ theorem considering output error, input error and model uncertainty
H Rappel, LAA Beex, L Noels, SPA Bordas
Probabilistic Engineering Mechanics 55, 28-41, 2019
Bayesian inference to identify parameters in viscoelasticity
H Rappel, LAA Beex, SPA Bordas
Mechanics of Time-Dependent Materials 22, 221-258, 2018
Bayesian identification of mean-field homogenization model parameters and uncertain matrix behavior in non-aligned short fiber composites
M Mohamedou, K Zulueta, CN Chung, H Rappel, L Beex, L Adam, ...
Composite Structures 220, 64-80, 2019
Estimating fibres’ material parameter distributions from limited data with the help of Bayesian inference
H Rappel, LAA Beex
European Journal of Mechanics-A/Solids 75, 169-196, 2019
Bayesian inference for the stochastic identification of elastoplastic material parameters: introduction, misconceptions and insights
H Rappel, LAA Beex, JS Hale, S Bordas
arXiv preprint arXiv:1606.02422, 2016
A Bayesian framework to identify random parameter fields based on the copula theorem and Gaussian fields: Application to polycrystalline materials
H Rappel, L Wu, L Noels, LAA Beex
Journal of Applied Mechanics 86 (12), 121009, 2019
Electromechanical properties identification for groups of piezoelectric energy harvester based on Bayesian inference
P Peralta, RO Ruiz, H Rappel, SPA Bordas
Mechanical Systems and Signal Processing 162, 108034, 2022
Model selection and sensitivity analysis in the biomechanics of soft tissues: A case study on the human knee meniscus
E Elmukashfi, G Marchiori, M Berni, G Cassiolas, NF Lopomo, H Rappel, ...
Advances in applied mechanics 55, 425-511, 2022
Full-field order-reduced Gaussian Process emulators for nonlinear probabilistic mechanics
C Ding, H Rappel, T Dodwell
Computer Methods in Applied Mechanics and Engineering 405, 115855, 2023
Numerical time-domain modeling of lamb wave propagation using elastodynamic finite integration technique
H Rappel, A Yousefi-Koma, J Jamali, A Bahari
Shock and Vibration 2014, 2014
Intercorrelated random fields with bounds and the Bayesian identification of their parameters: Application to linear elastic struts and fibers
H Rappel, M Girolami, LAA Beex
International Journal for Numerical Methods in Engineering, 2022
Model and parameter identification through Bayesian inference in solid mechanics
H Rappel
University of Luxembourg, Esch-sur-Alzette, Luxembourg, 2018
Identifying fibre material parameter distributions with little experimental efforts
H Rappel, L Beex, S Bordas
Multi-scale methods for fracture: model learning across scales, digital twinning and factors of safety: primer on Bayesian Inference
S Bordas, J Hale, L Beex, H Rappel, P Kerfriden, O Goury, A Akbari
Shape control of Bio-inspired tail by shape memory alloy actuator: an experimental study
H Rappel, A Yousefi-Koma, H Baseri
Functional order-reduced Gaussian Processes based machine-learning emulators for probabilistic constitutive modelling
C Ding, Y Chen, H Rappel, T Dodwell
Composites Part A: Applied Science and Manufacturing 173, 107695, 2023
A probabilistic peridynamic framework with an application to the study of the statistical size effect
M Hobbs, H Rappel, T Dodwell
arXiv preprint arXiv:2212.04415, 2022
Probabilistic modeling natural way to treat data
H Rappel
Geometrical and material uncertainties for the mechanics of composites
J Barbosa, S Bordas, A Carvalho, C Ding, H Lian, MA Loja, T Mathew, ...
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