| 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 | 113 | 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 | 90 | 2019 |
| Bayesian inference to identify parameters in viscoelasticity H Rappel, LAA Beex, SPA Bordas Mechanics of Time-Dependent Materials 22, 221-258, 2018 | 79 | 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 | 36 | 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 | 28 | 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 | 23 | 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 | 17 | 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 | 12 | 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 | 11 | 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 | 5 | 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 | 3 | 2014 |
| 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 | 1 | 2023 |
| 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 | 1 | 2022 |
| Model and parameter identification through Bayesian inference in solid mechanics H Rappel University of Luxembourg, Esch-sur-Alzette, Luxembourg, 2018 | 1 | 2018 |
| Identifying fibre material parameter distributions with little experimental efforts H Rappel, L Beex, S Bordas | 1 | 2018 |
| 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 EMPA High-performance Multiscale-Scale Day, 2015 | 1 | 2015 |
| Shape control of Bio-inspired tail by shape memory alloy actuator: an experimental study H Rappel, A Yousefi-Koma, H Baseri | 1 | 2014 |
| A probabilistic peridynamic framework with an application to the study of the statistical size effect M Hobbs, H Rappel, T Dodwell Applied Mathematical Modelling 128, 137-153, 2024 | | 2024 |
| Probabilistic modeling natural way to treat data H Rappel | | 2019 |
| Geometrical and material uncertainties for the mechanics of composites J Barbosa, S Bordas, A Carvalho, C Ding, H Lian, MA Loja, T Mathew, ... | | 2019 |
L.A.A. BeexUniversity of LuxembourgVerified email at uni.lu
