most citedA Bayesian regularization-backpropagation neural network model for peeling computations

88 citations

5 papers

cs.CE2020★ 88 cited

A Bayesian regularization-backpropagation neural network model for peeling computations

Saipraneeth Gouravaraju, Jyotindra Narayan, Roger A. Sauer +1

Bayesian regularization-backpropagation neural network (BR-BPNN) model is employed to predict some aspects of the gecko spatula peeling viz. the variation of the maximum normal and…

cs.CE2020★ 46 cited

On topology optimization of large deformation contact-aided shape morphing compliant mechanisms

Prabhat Kumar, Roger A. Sauer, Anupam Saxena

A topology optimization approach for designing large deformation contact-aided shape morphing compliant mechanisms is presented. Such mechanisms can be used in varying operating co…

cs.CE2020★ 81 cited

Contact with coupled adhesion and friction: Computational framework, applications, and new insights

Janine C. Mergel, Julien Scheibert, Roger A. Sauer

Contact involving soft materials often combines dry adhesion, sliding friction, and large deformations. At the local level, these three aspects are rarely captured simultaneously,…

cs.CE2020★ 25 cited

Isogeometric continuity constraints for multi-patch shells governed by fourth-order deformation and phase field models

Karsten Paul, Christopher Zimmermann, Thang X. Duong +1

This work presents numerical techniques to enforce continuity constraints on multi-patch surfaces for three distinct problem classes. The first involves structural analysis of thin…

physics.geo-ph2019★ 13 cited

Topographic uncertainty quantification for flow-like landslide models via stochastic simulations

Hu Zhao, Julia Kowalski

Topography representing digital elevation models (DEMs) are essential inputs for computational models capable of simulating the run-out of flow-like landslides. Yet, DEMs are often…