Hussein Rappel
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View article: Probabilistic Forecasting and Anomaly Detection in Sewer Systems Using Gaussian Processes
Probabilistic Forecasting and Anomaly Detection in Sewer Systems Using Gaussian Processes Open
This study investigates the capability of Gaussian process regression (GPR) models in the probabilistic forecasting of water flow and depth in a combined sewer system. Traditionally, deterministic methods have been implemented in sewer flo…
View article: Probabilistic Forecasting and Anomaly Detection in Sewer Systems Using Gaussian Processes
Probabilistic Forecasting and Anomaly Detection in Sewer Systems Using Gaussian Processes Open
This study investigates the capability of Gaussian process regression (GPR) models in probabilistic forecasting of water flow and depth in a combined sewer system. Traditionally, deterministic methods have been implemented in sewer flow fo…
View article: Extreme Precipitation Events Characteristics in West Java Region, Indonesia
Extreme Precipitation Events Characteristics in West Java Region, Indonesia Open
Climate change is increasing the frequency and intensity of extreme weather events worldwide, with West Java, Indonesia, particularly vulnerable to intense precipitation. These events contribute significantly to hydrometeorological disaste…
View article: Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids
Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids Open
Many real-world applications demand accurate and fast predictions, as well as reliable uncertainty estimates. However, quantifying uncertainty on high-dimensional predictions is still a severely under-investigated problem, especially when …
View article: A probabilistic peridynamic framework with an application to the study of the statistical size effect
A probabilistic peridynamic framework with an application to the study of the statistical size effect Open
This is the final version. Available from Elsevier via the DOI in this record.
View article: A probabilistic peridynamic framework with an application to the study of the statistical size effect
A probabilistic peridynamic framework with an application to the study of the statistical size effect Open
Mathematical models are essential for understanding and making predictions about systems arising in nature and engineering. Yet, mathematical models are a simplification of true phenomena, thus making predictions subject to uncertainty. He…
View article: Intercorrelated random fields with bounds and the Bayesian identification of their parameters: Application to linear elastic struts and fibers
Intercorrelated random fields with bounds and the Bayesian identification of their parameters: Application to linear elastic struts and fibers Open
Many materials and structures consist of numerous slender struts or fibers. Due to the manufacturing processes of different types of struts and the growth processes of natural fibers, their mechanical response frequently fluctuates from st…
View article: Model selection and sensitivity analysis in the biomechanics of soft tissues: a case study on the human knee meniscus
Model selection and sensitivity analysis in the biomechanics of soft tissues: a case study on the human knee meniscus Open
Soft tissues - such as ligaments and tendons - primarily consist of solid (collagen, predominantly) and liquid phases. Understanding the interaction between such components and how they change under physiological loading sets the basis for…
View article: A Bayesian Framework to Identify Random Parameter Fields Based on the Copula Theorem and Gaussian Fields: Application to Polycrystalline Materials
A Bayesian Framework to Identify Random Parameter Fields Based on the Copula Theorem and Gaussian Fields: Application to Polycrystalline Materials Open
For many models of solids, we frequently assume that the material parameters do not vary in space nor that they vary from one product realization to another. If the length scale of the application approaches the length scale of the microst…
View article: Model and parameter identification through Bayesian inference in solid mechanics
Model and parameter identification through Bayesian inference in solid mechanics Open
Predicting the behaviour of various engineering systems is commonly performed using mathematical models. These mathematical models include application-specific parameters that must be identified from measured data. The identification of mo…
View article: Bayesian inference for the stochastic identification of elastoplastic material parameters: Introduction, misconceptions and additional insight.
Bayesian inference for the stochastic identification of elastoplastic material parameters: Introduction, misconceptions and additional insight. Open
We discuss Bayesian inference (BI) for the probabilistic identification of material parameters. This contribution aims to shed light on the use of BI for the identification of elastoplastic material parameters. For this purpose a single sp…
View article: Bayesian inference for material parameter identification in elastoplasticity
Bayesian inference for material parameter identification in elastoplasticity Open
aFaculte des Sciences, de la Technologies et de la Communication, Universite du Luxembourg, Luxembourg. Email: {hussein.rappel, lars.beex, jack.hale and stephane.bordas}@uni.lu bDepartment of Aerospace and Mechanical Engineering, Universit…
View article: A Bayesian approach for parameter identification in elastoplasticity
A Bayesian approach for parameter identification in elastoplasticity Open
In computational mechanics, approaches based on error minimisation (e.g. the least squares method), are frequently used to identify material parameters based on experimental data. An alternative approach is Bayesian inference which gives a…
View article: Bayesian inference for the stochastic identification of elastoplastic material parameters: Introduction, misconceptions and insights
Bayesian inference for the stochastic identification of elastoplastic material parameters: Introduction, misconceptions and insights Open
We discuss Bayesian inference (BI) for the probabilistic identification of material parameters. This contribution aims to shed light on the use of BI for the identification of elastoplastic material parameters. For this purpose a single sp…
View article: Multi-scale methods for fracture: model learning across scales, digital twinning and factors of safety
Multi-scale methods for fracture: model learning across scales, digital twinning and factors of safety Open
Authors: S. P. A. Bordas, L. A. A. Beex, P. Kerfriden, D. A. Paladim, O. Goury, A. Akbari, H. Rappel Multi-scale methods for fracture: model learning across scales, digital twinning and factors of safety Fracture and material instabilities…