Hussein Rappel

525 total citations
18 papers, 371 citations indexed

About

Hussein Rappel is a scholar working on Civil and Structural Engineering, Statistics, Probability and Uncertainty and Statistical and Nonlinear Physics. According to data from OpenAlex, Hussein Rappel has authored 18 papers receiving a total of 371 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Civil and Structural Engineering, 9 papers in Statistics, Probability and Uncertainty and 6 papers in Statistical and Nonlinear Physics. Recurrent topics in Hussein Rappel's work include Probabilistic and Robust Engineering Design (9 papers), Structural Health Monitoring Techniques (6 papers) and Model Reduction and Neural Networks (5 papers). Hussein Rappel is often cited by papers focused on Probabilistic and Robust Engineering Design (9 papers), Structural Health Monitoring Techniques (6 papers) and Model Reduction and Neural Networks (5 papers). Hussein Rappel collaborates with scholars based in Luxembourg, United Kingdom and Belgium. Hussein Rappel's co-authors include Lars Beex, Stéphane Bordas, Ludovic Noels, Jack Hale, Tim Dodwell, Chensen Ding, Ling Wu, Rafael O. Ruiz, Zoltán Major and Jakub Lengiewicz and has published in prestigious journals such as Computer Methods in Applied Mechanics and Engineering, Journal of Applied Mechanics and International Journal for Numerical Methods in Engineering.

In The Last Decade

Hussein Rappel

17 papers receiving 365 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Hussein Rappel Luxembourg	9	140	128	105	92	62	18	371
Amélie Fau France	12	198 1.4×	133 1.0×	121 1.2×	75 0.8×	46 0.7×	33	446
James E. Warner United States	10	134 1.0×	151 1.2×	112 1.1×	108 1.2×	24 0.4×	33	388
Shengwen Yin China	12	259 1.9×	236 1.8×	74 0.7×	60 0.7×	116 1.9×	32	415
Rengui Bi China	12	266 1.9×	226 1.8×	143 1.4×	84 0.9×	34 0.5×	23	493
Kirubel Teferra United States	12	116 0.8×	69 0.5×	79 0.8×	121 1.3×	50 0.8×	24	384
Anh Tran United States	12	96 0.7×	54 0.4×	67 0.6×	103 1.1×	28 0.5×	35	457
Rubén Ibáñez France	11	138 1.0×	114 0.9×	189 1.8×	139 1.5×	159 2.6×	23	568
Sonjoy Das United States	9	153 1.1×	89 0.7×	52 0.5×	57 0.6×	51 0.8×	23	390
Michel Fogli France	11	139 1.0×	147 1.1×	102 1.0×	61 0.7×	20 0.3×	24	338

Countries citing papers authored by Hussein Rappel

Since Specialization

Citations

This map shows the geographic impact of Hussein Rappel's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hussein Rappel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hussein Rappel more than expected).

Fields of papers citing papers by Hussein Rappel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hussein Rappel. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hussein Rappel. The network helps show where Hussein Rappel may publish in the future.

Co-authorship network of co-authors of Hussein Rappel

This figure shows the co-authorship network connecting the top 25 collaborators of Hussein Rappel. A scholar is included among the top collaborators of Hussein Rappel based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hussein Rappel. Hussein Rappel is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

Rappel, Hussein, et al.. (2025). Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids. Computer Methods in Applied Mechanics and Engineering. 437. 117790–117790. 11 indexed citations

Shen, Jiajia, et al.. (2025). Re-programmable mechanical metamaterials for tunable energy dissipation through pre-stressing: Concept, mechanics and data-driven design. Thin-Walled Structures. 216. 113601–113601. 4 indexed citations

Bradshaw, Catherine P., et al.. (2025). Extreme Precipitation Events Characteristics in West Java Region, Indonesia. IOP Conference Series Earth and Environmental Science. 1472(1). 12034–12034.

Rappel, Hussein, et al.. (2024). A probabilistic peridynamic framework with an application to the study of the statistical size effect. Applied Mathematical Modelling. 128. 137–153. 3 indexed citations

Ding, Chensen, Hussein Rappel, & Tim Dodwell. (2023). Full-field order-reduced Gaussian Process emulators for nonlinear probabilistic mechanics. Computer Methods in Applied Mechanics and Engineering. 405. 115855–115855. 13 indexed citations

Ding, Chensen, Yang Chen, Hussein Rappel, & Tim Dodwell. (2023). Functional order-reduced Gaussian Processes based machine-learning emulators for probabilistic constitutive modelling. Composites Part A Applied Science and Manufacturing. 173. 107695–107695. 7 indexed citations

Rappel, Hussein, Mark Girolami, & Lars Beex. (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. 123(15). 3418–3463. 3 indexed citations

Ruiz, Rafael O., et al.. (2021). Electromechanical properties identification for groups of piezoelectric energy harvester based on Bayesian inference. Mechanical Systems and Signal Processing. 162. 108034–108034. 15 indexed citations

Rappel, Hussein, Lars Beex, Jack Hale, Ludovic Noels, & Stéphane Bordas. (2019). A Tutorial on Bayesian Inference to Identify Material Parameters in Solid Mechanics. Archives of Computational Methods in Engineering. 27(2). 361–385. 109 indexed citations

10.

Rappel, Hussein, Ling Wu, Ludovic Noels, & Lars Beex. (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. 86(12). 13 indexed citations

11.

Rappel, Hussein & Lars Beex. (2019). Estimating fibres’ material parameter distributions from limited data with the help of Bayesian inference. European Journal of Mechanics - A/Solids. 75. 169–196. 25 indexed citations

12.

Rappel, Hussein, et al.. (2019). Bayesian identification of Mean-Field Homogenization model parameters and uncertain matrix behavior in non-aligned short fiber composites. Composite Structures. 220. 64–80. 27 indexed citations

13.

Rappel, Hussein, Lars Beex, Ludovic Noels, & Stéphane Bordas. (2018). Identifying elastoplastic parameters with Bayes’ theorem considering output error, input error and model uncertainty. Probabilistic Engineering Mechanics. 55. 28–41. 70 indexed citations

14.

Rappel, Hussein, Lars Beex, & Stéphane Bordas. (2017). Bayesian inference to identify parameters in viscoelasticity. Mechanics of Time-Dependent Materials. 22(2). 221–258. 67 indexed citations

15.

Bordas, Stéphane, et al.. (2015). Multi-scale methods for fracture: model learning across scales, digital twinning and factors of safety : primer on Bayesian Inference. Open Repository and Bibliography (University of Luxembourg). 1 indexed citations

16.

Rappel, Hussein, Aghil Yousefi‐Koma, & Hamid Baseri. (2014). Shape control of Bio-inspired tail by shape memory alloy actuator: an experimental study. Open Repository and Bibliography (University of Luxembourg). 1 indexed citations

17.

Rappel, Hussein, et al.. (2014). Numerical Time-Domain Modeling of Lamb Wave Propagation Using Elastodynamic Finite Integration Technique. Shock and Vibration. 2014. 1–6. 1 indexed citations

18.

Rappel, Hussein, et al.. (2014). Shape Control of a Bio-Inspired Flexible Tail by a Shape Memory Alloy Actuator: an Experimental Study. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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