Overview
Hussein Rappel is a Lecturer (Assistant Professor) in Computational Engineering at the Department of Engineering University of Exeter. He did his Ph.D. in Computational Sciences at the University of Luxembourg (Luxembourg) as a member of the Legato Team and at the University of Liege (Belgium) as a member of the Computational & Multi-scale Mechanics of Materials (CM3) unit.
Prior to joining the University of Exeter, he was a postdoctoral researcher at the Alan Turing Institute and a member of the Computational Statistics and Machine Learning Group (CSML) at the University of Cambridge.
Research interests
Broadly speaking Dr. Rappel is interested in probabilistic and statistical modeling and their intersection with engineering problems. A list of his publications can be found here ==> the link.
Teaching activity
ECM1201-Mathematics for Engineers
ENG1007-Fundamentals of Mechanics
Student projects:
ECM3175-Individual Project
ECM3174-Engineering Year in Industry
Research opportunities
I am always looking for ambitious, driven, and passionate Ph.D. students. If you are interested please get in touch. I will be happy to speak to you.
Available positions:
If you are interested in doing your Ph.D. in applied mathematics, probabilistic modeling, and computational engineering at The City University of Hong Kong (CityU) or The Hong Kong Polytechnic University (PolyU), please send me your CV.
Publications
Copyright Notice: Any articles made available for download are for personal use only. Any other use requires prior permission of the author and the copyright holder.
| 2024 | 2023 | 2022 | 2020 | 2019 | 2018 | 2016 | 2014 |
2024
- Hobbs M, Rappel H, Dodwell T. (2024) A probabilistic peridynamic framework with an application to the study of the statistical size effect, Applied Mathematical Modelling, volume 128, pages 137-153, DOI:10.1016/j.apm.2024.01.004.
2023
- Ding C, Chen Y, Rappel H, Dodwell T. (2023) Functional order-reduced Gaussian Processes based machine-learning emulators for probabilistic constitutive modelling, Composites Part A: Applied Science and Manufacturing, volume 173, pages 107695-107695, article no. 107695, DOI:10.1016/j.compositesa.2023.107695. [PDF]
- Ding C, Rappel H, Dodwell T. (2023) Full-field order-reduced Gaussian Process emulators for nonlinear probabilistic mechanics, Computer Methods in Applied Mechanics and Engineering, volume 405, DOI:10.1016/j.cma.2022.115855.
2022
- Hobbs M, Rappel H, Dodwell T. (2022) A probabilistic peridynamic framework with an application to the study of the statistical size effect. [PDF]
- Elmukashfi E, Marchiori G, Berni M, Cassiolas G, Lopomo NF, Rappel H, Girolami M, Barrera O. (2022) Chapter Five Model selection and sensitivity analysis in the biomechanics of soft tissues: A case study on the human knee meniscus, volume 55, pages 425-511, DOI:10.1016/bs.aams.2022.05.001.
- Rappel H, Girolami M, Beex LAA. (2022) Intercorrelated random fields with bounds and the Bayesian identification of their parameters: Application to linear elastic struts and fibers, International Journal for Numerical Methods in Engineering, volume 123, no. 15, pages 3418-3463, DOI:10.1002/nme.6974. [PDF]
- Elmukashfi E, Marchiori G, Berni M, Cassiolas G, Lopomo NF, Rappel H, Girolami M, Barrera O. (2022) Model selection and sensitivity analysis in the biomechanics of soft tissues: a case study on the human knee meniscus, Elsevier.
- Peralta P, Ruiz OR, Rappel H, Bordas PAS. (2022) Electromechanical properties identification for groups of piezoelectric energy harvester based on Bayesian inference, MECHANICAL SYSTEMS AND SIGNAL PROCESSING, volume 162, article no. ARTN 108034, DOI:10.1016/j.ymssp.2021.108034. [PDF]
2020
- Rappel H, Beex LAA, Hale JS, Noels L, Bordas SPA. (2020) A Tutorial on Bayesian Inference to Identify Material Parameters in Solid Mechanics, ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, volume 27, no. 2, pages 361-385, DOI:10.1007/s11831-018-09311-x. [PDF]
2019
- Rappel H, Beex LAA, Noels L, Bordas SPA. (2019) Identifying elastoplastic parameters with Bayes' theorem considering output error, input error and model uncertainty, PROBABILISTIC ENGINEERING MECHANICS, volume 55, pages 28-41, DOI:10.1016/j.probengmech.2018.08.004. [PDF]
- Mohamedou M, Zulueta K, Chung CN, Rappel H, Beex L, Adam L, Arriaga A, Major Z, Wu L, Noels L. (2019) Bayesian identification of Mean-Field Homogenization model parameters and uncertain matrix behavior in non-aligned short fiber composites, COMPOSITE STRUCTURES, volume 220, pages 64-80, DOI:10.1016/j.compstruct.2019.03.066. [PDF]
- Rappel H, Beex LAA. (2019) Estimating fibres' material parameter distributions from limited data with the help of Bayesian inference, EUROPEAN JOURNAL OF MECHANICS A-SOLIDS, volume 75, pages 169-196, DOI:10.1016/j.euromechsol.2019.01.001. [PDF]
- Rappel H, Wu L, Noels L, Beex LAA. (2019) A Bayesian Framework to Identify Random Parameter Fields Based on the Copula Theorem and Gaussian Fields: Application to Polycrystalline Materials, JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME, volume 86, no. 12, article no. ARTN 121009, DOI:10.1115/1.4044894. [PDF]
2018
- Rappel H, Beex LAA, Bordas SPA. (2018) Bayesian inference to identify parameters in viscoelasticity, MECHANICS OF TIME-DEPENDENT MATERIALS, volume 22, no. 2, pages 221-258, DOI:10.1007/s11043-017-9361-0. [PDF]
2016
- Rappel H, Beex LAA, Hale JS, Bordas SPA. (2016) Bayesian inference for the stochastic identification of elastoplastic material parameters: Introduction, misconceptions and insights. [PDF]
2014
- Rappel H, Yousefi-Koma A, Jamali J, Bahari A. (2014) Numerical Time-Domain Modeling of Lamb Wave Propagation Using Elastodynamic Finite Integration Technique, SHOCK AND VIBRATION, volume 2014, article no. ARTN 434187, DOI:10.1155/2014/434187. [PDF]
