Elouneg, A., Sutula, D., SENSALE, M., Chouly, F., Chambert, J., Lejeune, A., BAROLI, D., HAUSEUX, P., BORDAS, S., & Jacquet, E. (09 December 2019). Mechanical parameters identification of keloid and surrounding healthy skin using Digital Image Correlation measurements in vivo [Paper presentation]. 24ème Congrès Français de Mécanique. |
Sutula, D., Elouneg, A., SENSALE, M., Chouly, F., Chambert, J., Lejeune, A., BAROLI, D., HAUSEUX, P., BORDAS, S., & Jacquet, E. (09 December 2019). Parameter identification problem in bimaterial human skin and sensitivity analysis : Uncertainties in biomechanics of skin [Paper presentation]. 24ème Congrès Français de Mécanique. |
HAUSEUX, P., HALE, J., BULLE, R., Chouly, F., Lozinski, A., & BORDAS, S. (23 July 2018). Uncertainty Quantification in Finite Element Models:Application to SoftTissue Biomechanics [Paper presentation]. 13th World Congress in Computational Mechanics (WCCM XIII). |
HALE, J., HAUSEUX, P., & BORDAS, S. (08 January 2018). Using higher-order adjoints to accelerate the solution of UQ problems with random fields [Poster presentation]. Key UQ methodologies and motivating applications, Cambridge, United Kingdom. |
HAUSEUX, P., HALE, J., Cotin, S., & BORDAS, S. (2018). Quantifying the uncertainty in a hyperelastic soft tissue model with stochastic parameters. Applied Mathematical Modelling, 62, 86-102. doi:10.1016/j.apm.2018.04.021 Peer Reviewed verified by ORBi |
HAUSEUX, P., & BORDAS, S. (2018). Final Report IRP MOmENTUM Needle insertion simulation. UL. https://orbilu.uni.lu/handle/10993/36295 |
HAUSEUX, P., HALE, J., & BORDAS, S. (20 December 2017). Calculating the Malliavin derivative of some stochastic mechanics problems. PLoS ONE, 12 (12), 0189994. doi:10.1371/journal.pone.0189994 Peer Reviewed verified by ORBi |
HAUSEUX, P., HALE, J., & BORDAS, S. (September 2017). Uncertainty Quantification (Monte Carlo methods) - Sensitivity Analysis - Biomechanics [Paper presentation]. Legato Team seminar. |
HAUSEUX, P., HALE, J., & BORDAS, S. (01 May 2017). Accelerating Monte Carlo estimation with derivatives of high-level finite element models. Computer Methods in Applied Mechanics and Engineering, 318, 917-936. doi:10.1016/j.cma.2017.01.041 Peer reviewed |
HAUSEUX, P., HALE, J., & BORDAS, S. (February 2017). Uncertainty Quantification - Sensitivity Analysis / Biomechanics [Paper presentation]. Groupe de Travail, Besançon, France. |
BAROLI, D., HAUSEUX, P., HALE, J., & BORDAS, S. (12 December 2016). Image to analysis pipeline: single and double balloons kyphoplasty [Poster presentation]. Residential Workshop on Computational Sciences for Medical Simulation. |
HAUSEUX, P., HALE, J., & BORDAS, S. (December 2016). Uncertainty quantification for soft tissue biomechanics [Poster presentation]. Computational Sciences for Medicine Workshop 2016 Dec 12-14 Luxembourg. |
HAUSEUX, P., HALE, J., & BORDAS, S. (September 2016). Stochastic FE analysis of brain deformation with different hyper-elastic models [Paper presentation]. Computer Methods in Biomechanics and Biomedical Engineering, Tel Aviv, Israel. |
HAUSEUX, P., Roubin, E., Seyedi, D. M., & Colliat, J.-B. (September 2016). FE modelling with strong discontinuities for 3D tensile and shear fractures: Application to underground excavation. Computer Methods in Applied Mechanics and Engineering, 309, 269–287. doi:10.1016/j.cma.2016.05.014 Peer reviewed |
HAUSEUX, P., HALE, J., & BORDAS, S. (June 2016). Efficient propagation of uncertainty through an inverse non-linear deformation model of soft tissue [Paper presentation]. European Congress on Computational Methods in Applied Sciences and Engineering. |
HAUSEUX, P., HALE, J., & BORDAS, S. (09 May 2016). Propagating uncertainty using FE advanced Monte-Carlo methods: application to non- linear hyperelastic models [Paper presentation]. internal report. |
HAUSEUX, P., HALE, J., & BORDAS, S. (May 2016). Propagating uncertainty through a non-linear hyperelastic model using advanced Monte-Carlo methods [Paper presentation]. The FEniCS'16 workshop, Oslo, Norway. |
HAUSEUX, P., HALE, J., & BORDAS, S. (n.d.). Solving the stochastic Burgers equation with a sensitivity derivative-driven Monte Carlo method. doi:10.6084/m9.figshare.3561306 |