Explore the words cloud of the DRIVEN project. It provides you a very rough idea of what is the project "DRIVEN" about.
The following table provides information about the project.
UNIVERSITE DU LUXEMBOURG
|Coordinator Country||Luxembourg [LU]|
|Total cost||1˙157˙475 €|
|EC max contribution||1˙000˙000 € (86%)|
1. H2020-EU.4.b. (Twinning of research institutions)
|Duration (year-month-day)||from 2018-10-01 to 2021-09-30|
Take a look of project's partnership.
|1||UNIVERSITE DU LUXEMBOURG||LU (ESCH-SUR-ALZETTE)||coordinator||512˙550.00|
|2||UNIVERSITY OF LIMERICK||IE (LIMERICK)||participant||245˙000.00|
|3||INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE||FR (LE CHESNAY CEDEX)||participant||242˙450.00|
|4||THE UNIVERSITY OF TEXAS SYSTEM||US (AUSTIN)||participant||0.00|
The overall aim of the DRIVEN project is to boost the scientific excellence and technology-transfer capacity in data-driven simulation of the University of Luxembourg (UL) by creating a network with the high-quality Twinning partners: Institut National de Recherche en Informatique et en Automatique (Inria), University of Limerick (ULIM) and University of Texas at Austin (UT Austin). To achieve this aim, the 3 year project will build upon the existing strong research and innovation base of UL and its Twinning partners.
To boost their scientific excellence and technology transfer capacity in data-driven simulation, the partners will implement a research and innovation strategy focused on three sub-topics:
1. Mathematical foundations for data-driven simulations – UL with UT Austin, 2. Data-driven simulations for computer-assisted therapy – UL with Inria, and 3. Data-driven simulations for functional composite materials – UL with ULIM.
The research and innovation strategy takes into account the recent SWOT analysis of UL and has the following objectives:
• Objective 1: Strengthen UL’s research excellence in data-driven simulation • Objective 2: Enhance the research and innovation capacity of UL and the Twinning partners • Objective 3: Raise the research profile of UL and the Twinning Partners • Objective 4: Contribute to the SMART Specialisation Strategy of Luxembourg • Objective 5: Support research and innovation on a European level In order to achieve these objectives, the consortium partners will implement a comprehensive set of measures via the project’s work packages:
• Short term staff exchanges (WP1); • Training workshops, conferences and summer schools (WP2); • Dissemination and outreach (WP3).
|Project leaflet and poster||Websites, patent fillings, videos etc.||2019-09-20 14:27:40|
Take a look to the deliverables list in detail: detailed list of DRIVEN deliverables.
|year||authors and title||journal||last update|
H. Rappel, L.A.A. Beex, L. Noels, S.P.A. Bordas
Identifying elastoplastic parameters with Bayesâ€™ theorem considering output error, input error and model uncertainty
published pages: , ISSN: 0266-8920, DOI: 10.1016/j.probengmech.2018.08.004
|Probabilistic Engineering Mechanics||2019-05-19|
Paul Hauseux, Jack S. Hale, StÃ©phane Cotin, StÃ©phane P.A. Bordas
Quantifying the uncertainty in a hyperelastic soft tissue model with stochastic parameters
published pages: 86-102, ISSN: 0307-904X, DOI: 10.1016/j.apm.2018.04.021
|Applied Mathematical Modelling 62||2019-05-19|
Paul Hauseux, Jack S. Hale, StÃ©phane P. A. Bordas
Calculating the Malliavin derivative of some stochastic mechanics problems
published pages: e0189994, ISSN: 1932-6203, DOI: 10.1371/journal.pone.0189994
|PLOS ONE 12/12||2019-05-19|
K.C. Hoang, P. Kerfriden, S.P.A. Bordas
A fast, certified and â€œtuning freeâ€ two-field reduced basis method for the metamodelling of affinely-parametrised elasticity problems
published pages: 121-158, ISSN: 0045-7825, DOI: 10.1016/j.cma.2015.08.016
|Computer Methods in Applied Mechanics and Engineering 298||2019-05-19|
Hussein Rappel, Lars A. A. Beex, StÃ©phane P. A. Bordas
Bayesian inference to identify parameters in viscoelasticity
published pages: 221-258, ISSN: 1385-2000, DOI: 10.1007/s11043-017-9361-0
|Mechanics of Time-Dependent Materials 22/2||2019-05-19|
Paul Hauseux, Jack S. Hale, StÃ©phane P.A. Bordas
Accelerating Monte Carlo estimation with derivatives of high-level finite element models
published pages: 917-936, ISSN: 0045-7825, DOI: 10.1016/j.cma.2017.01.041
|Computer Methods in Applied Mechanics and Engineering 318||2019-05-19|
Huu Phuoc Bui, Satyendra Tomar, Hadrien Courtecuisse, Stephane Cotin, Stephane P. A. Bordas
Real-Time Error Control for Surgical Simulation
published pages: 596-607, ISSN: 0018-9294, DOI: 10.1109/tbme.2017.2695587
|IEEE Transactions on Biomedical Engineering 65/3||2019-05-19|
Olivier Goury, David Amsallem, StÃ©phane Pierre Alain Bordas, Wing Kam Liu, Pierre Kerfriden
Automatised selection of load paths to construct reduced-order models in computational damage micromechanics: from dissipation-driven random selection to Bayesian optimization
published pages: 213-234, ISSN: 0178-7675, DOI: 10.1007/s00466-016-1290-2
|Computational Mechanics 58/2||2019-05-19|
Tuan Nguyen, Alireza Kashani, Tuan Ngo, StÃ©phane Bordas
Deep neural network with high-order neuron for the prediction of foamed concrete strength
published pages: , ISSN: 1093-9687, DOI: 10.1111/mice.12422
|Computer-Aided Civil and Infrastructure Engineering||2019-05-19|
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