activity
20162026
most citedGaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids

31 citations · 31 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CE2026

A 2.5D NURBS-Trace Infinite-Element Method for Moving-Load Wave Propagation and Soil--Structure Interaction in Semi-Infinite Ground

Yanhui Zhong, Hao Hong, Bei Zhang +3

For moving-load problems whose geometry and material properties are approximately invariant along the traveling direction, 2.5D analysis retains three displacement components at lo…

cs.CE2024★ 31 cited

Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids

Saurabh Deshpande, Hussein Rappel, Mark Hobbs +2

Many real-world applications demand accurate and fast predictions, as well as reliable uncertainty estimates. However, quantifying uncertainty on high-dimensional predictions is st…

math.NA2022

A probabilistic peridynamic framework with an application to the study of the statistical size effect

Mark Hobbs, Hussein Rappel, Tim Dodwell

Mathematical models are essential for understanding and making predictions about systems arising in nature and engineering. Yet, mathematical models are a simplification of true ph…

physics.med-ph2021

Model selection and sensitivity analysis in the biomechanics of soft tissues: a case study on the human knee meniscus

Elsiddig Elmukashfi, Gregorio Marchiori, Matteo Berni +5

Soft tissues - such as ligaments and tendons - primarily consist of solid (collagen, predominantly) and liquid phases. Understanding the interaction between such components and how…

cs.CE2016

Bayesian inference for the stochastic identification of elastoplastic material parameters: Introduction, misconceptions and insights

Hussein Rappel, Lars A. A. Beex, Jack S. Hale +1

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 elas…