OBJECTIVES

The overall aim of the DRIVEN project was to boost the scientific excellence and innovation capacity in data-driven simulation of the Computational Sciences interdisciplinary group at the University of Luxembourg. The Twinning activities benefited the University of Luxembourg but also the other consortium partners. The project had a positive impact on numerous  industrial sectors in Luxembourg and in Europe such as Materials, Space Technology and Automotive.

SCIENTIFIC KNOWLEDGE

The partners implemented a research and innovation strategy focused on three sub-topics:

1. Mathematical foundations for data-driven simulations (University of Luxembourg and UT Austin)

• Geometry & Discretization
• Solution & Error Control
• Inverse Problems & Data Assimilation

2. Data-driven simulations for computer-assisted therapy (University of Luxembourg and Inria)

• Patient-specific organ discretizations
• Inverse problems
• Reduced Order Modelling

3. Data-driven simulations for functional composite materials (University of Luxembourg and ULIM)

• Multi-scale simulations for heterogeneous materials
• Experimental techniques for material characterisation and monitoring

RESEARCH PUBLICATIONS

The partners produced the following research publications during the course of the project:

• Chen, L, Berke, PZ, Massart, TJ, Beex, LA, Magliulo, M, Bordas, SP. A refinement indicator for adaptive quasicontinuum approaches for structural lattices. Int J Number Methods Eng. (2021); 122: 2498– 2527. https://doi.org/10.1002/nme.6629
• Marco Magliulo, Jakub Lengiewicz, Andreas Zilian, Lars A.A. Beex, Beam-inside-beam contact: Mechanical simulations of slender medical instruments inside the human body. Computer Methods and Programs in Biomedicine (2020); 196 105527. https://doi.org/10.1016/j.cmpb.2020.105527
• Farina, S., Claus, S., Hale, J.S. et al. A cut finite element method for spatially resolved energy metabolism models in complex neuro-cell morphologies with minimal remeshing. Adv. Model. and Simul. in Eng. Sci. 8, 5 (2021). https://doi.org/10.1186/s40323-021-00191-8
• R.Spencer, P.Gkinis, E.D.Koronaki, D.I.Gerogiorgis, S.P.A.Bordas, et al. Investigation of the chemical vapor deposition of Cu from copper amidinate through data driven efficient CFD modelling, Computers & Chemical Engineering, 149, 107289 (2021) https://doi.org/10.1016/j.compchemeng.2021.107289
• Alban Odot, Ryadh Haferssas, Stéphane Cotin, DeepPhysics: a physics aware deep learning framework for real‐time simulation (2022), International Journal for Numerical Methods in Engineering  DOI:10.1002/nme.6943