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See detailReduced basis Nitsche-based domain decomposition: a biomedical application
Baroli, Davide UL; Beex, Lars UL; Hale, Jack UL et al

Scientific Conference (2017, March 10)

Nowadays, the personalized biomedical simulations demand real-time efficient and reliable method to alleviate the computational complexity of high-fidelity simulation. In such applications, the necessity ... [more ▼]

Nowadays, the personalized biomedical simulations demand real-time efficient and reliable method to alleviate the computational complexity of high-fidelity simulation. In such applications, the necessity of solving different substructure, e.g. tissues or organs, with different numbers of the degrees of freedom and of coupling the reduced order spaces for each substructure poses a challenge in the on-fly simulation. In this talk, this challenge is taken into account employing the Nitsche-based domain decomposition technique inside the reduced order model [1]. This technique with respect to other domain decomposition approach allows obtaining a solution with the same accuracy of underlying finite element formulation and to flexibly treat interface with non-matching mesh. The robustness of the coupling is determined by the penalty coefficients that is chosen using ghost penalty technique [2]. Furthermore, to reduce the computational complexity of the on-fly assembling it is employed the empirical interpolation approach proposed in [3]. The numerical tests, performed using FEniCS[4], petsc4py and slepc4py [5], shows the good performance of the method and the reduction of computation cost. [1] Baroli, D., Beex L. and Bordas, S. Reduced basis Nitsche-based domain decomposition. In preparation. [2] Burman, E., Claus, S., Hansbo, P., Larson, M. G., & Massing, A. (2015). CutFEM: Discretizing geometry and partial differential equations. International Journal for Numerical Methods in Engineering, 104(7), 472-501. [3] E. Schenone, E., Beex,L., Hale, J.S., Bordas S. Proper Orthogonal Decomposition with reduced integration method. Application to nonlinear problems. In preparation. [4] A. Logg, K.-A. Mardal, G. N. Wells et al. Automated Solution of Differential Equations by the Finite Element Method, Springer 2012. [5] L. Dalcin, P. Kler, R. Paz, and A. Cosimo, Parallel Distributed Computing using Python, Advances in Water Resources, 34(9):1124-1139, 2011. http://dx.doi.org/10.1016/j.advwatres.2011.04.013 [less ▲]

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See detailUncertainty Quantification - Sensitivity Analysis / Biomechanics
Hauseux, Paul UL; Hale, Jack UL; Bordas, Stéphane UL

Presentation (2017, February)

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See detailProceedings of the FEniCS Conference 2017
Hale, Jack UL

Book published by Figshare (2017)

Proceedings of the FEniCS Conference 2017 that took place 12-14 June 2017 at the University of Luxembourg, Luxembourg.

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See detailImage to analysis pipeline: single and double balloons kyphoplasty
Baroli, Davide UL; Hauseux, Paul UL; Hale, Jack UL et al

Poster (2016, December 12)

In this work, we present a semi-automatic pipeline from image to simulation of a patient fractured vertebra after the kyphoplastic augmentation with two balloons. In this procedure, the CT-scan medical ... [more ▼]

In this work, we present a semi-automatic pipeline from image to simulation of a patient fractured vertebra after the kyphoplastic augmentation with two balloons. In this procedure, the CT-scan medical image are pre-processed using open-source software Slice3D for segmentation and 3D reconstruction operation. Then, using geometric processing the 3D surface geometry is enhanced to avoid degenerate element and trigging phenomena on vertebra and cement area. We perform a finite element analysis to evaluate the risk of subsequent vertebral fracture. Finally using Monte-Carlo technique, we assess the propagation of uncertainty of material parameter on the evaluation of this risk. Based on the developed semi-automatic pipelines, it is possible to perform a patient-specific simulation that assesses the successful of kyphoplasty operation. [less ▲]

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See detailElastography under uncertainty
Hale, Jack UL; Farrell, Patrick; Bordas, Stéphane UL

Poster (2016, December 12)

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See detailBayesian inference for parameter identification in computational mechanics
Rappel, Hussein UL; Beex, Lars UL; Hale, Jack UL et al

Poster (2016, December 12)

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See detailNumerical methods for fracture/cutting of heterogeneous materials
Sutula, Danas UL; Agathos, Konstantinos UL; Ziaei Rad, Vahid UL et al

Presentation (2016, December)

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See detailUncertainty quantification for soft tissue biomechanics
Hauseux, Paul UL; Hale, Jack UL; Bordas, Stéphane UL

Poster (2016, December)

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See detailBayesian inference for material parameter identification in elastoplasticity
Rappel, Hussein UL; Beex, Lars UL; Hale, Jack UL et al

Scientific Conference (2016, September 07)

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See detailStochastic FE analysis of brain deformation with different hyper-elastic models
Hauseux, Paul UL; Hale, Jack UL; Bordas, Stéphane UL

Scientific Conference (2016, September)

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See detailPOD-based reduction methods, the Quasicontinuum Method and their Resemblance
Beex, Lars UL; Schenone, Elisa; Hale, Jack UL

Scientific Conference (2016, June 27)

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See detailA Bayesian approach for parameter identification in elastoplasticity
Rappel, Hussein UL; Beex, Lars UL; Hale, Jack UL et al

Scientific Conference (2016, June 09)

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See detailPhase field approach to fracture: Towards the simulation of cutting soft tissues
Ziaei Rad, Vahid UL; Hale, Jack UL; Maurini, Corrado et al

Scientific Conference (2016, June 08)

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See detailPOD-based Reduction Methods, the Quasicontinuum Method and their Resemblance
Schenone, Elisa; Hale, Jack UL; Beex, Lars UL

Scientific Conference (2016, June)

POD-based reduction methods and the quasicontinuum method share two similar reduction steps to increase the computational speed of large mechanical models. Here, they are compared with each other.

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See detailA Bayesian approach for parameter identification in elastoplasticity
Rappel, Hussein UL; Beex, Lars UL; Hale, Jack UL et al

Scientific Conference (2016, June)

Detailed reference viewed: 171 (28 UL)
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See detailBayesian inference for material parameter identification
Rappel, Hussein UL; Beex, Lars UL; Hale, Jack UL et al

Report (2016)

Detailed reference viewed: 131 (14 UL)
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See detailOrchestrating clinical simulations with FEniCS
Weir, Phil; Ellerweg, Roland; Hale, Jack UL

Scientific Conference (2016, May)

Detailed reference viewed: 65 (4 UL)