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See detailData science meets computational mechanics
Dehghani, Hamidreza UL; Zilian, Andreas UL

Report (2021)

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See detailMaaS4All Project Report
Bandiera, Claudia UL; Cisterna, Carolina UL; Viti, Francesco UL

Report (2021)

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See detailData Centric Engineering and Data-Driven Modelling - Computational Engineering Lab Report 2019
Bordas, Stéphane UL; Peters, Bernhard UL; Viti, Francesco UL et al

Report (2019)

https://www.cambridge.org/core/journals/data-centric-engineering

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See detailComputational Sciences Year 2016 Activity Report
Bordas, Stéphane UL

Report (2016)

Born from a bottom-up initiative of Mathematics, Computer Science, Physics and Computational Engineering, Computational Sciences (CoSc) have contributed to create at UL a positive and symbiotic research ... [more ▼]

Born from a bottom-up initiative of Mathematics, Computer Science, Physics and Computational Engineering, Computational Sciences (CoSc) have contributed to create at UL a positive and symbiotic research environment relying on a strong fundamental scientific research core. CoSc will continue to rationalize research efforts across a range of strategic innovation domains by centralizing research and development tools and building upon the existing strengths of the Luxembourgish research and socio-economic landscape. [less ▲]

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See detailShape Optimization Directly from CAD: an Isogeometric Boundary Element Approach Using T-splines
Lian, Haojie; Pierre, Kerfriden; Bordas, Stéphane UL

Report (2016)

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See detail2015 Lab report - Legato report 001
Bordas, Stéphane UL

Report (2016)

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See detailComputational Mechanics Lab Report 2013-2014
Bordas, Stéphane UL

Report (2015)

This is the report of the Computational Mechanics Lab led by Prof. Stéphane Bordas

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See detailGlobal energy minimization for multiple fracture growth
Sutula, Danas; Bordas, Stéphane UL

Report (2013)

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See detailSpace-time goal-oriented reduced basis approximation for linear wave equation
Hoang, Khac Chi; Kerfriden, Pierre; Bordas, Stéphane UL

Report (2013)

In this paper, we study numerically the linear damped second-order hyperbolic partial differen-tial equation (PDE) with affine parameter dependence using a goal-oriented approach by finite element (FE ... [more ▼]

In this paper, we study numerically the linear damped second-order hyperbolic partial differen-tial equation (PDE) with affine parameter dependence using a goal-oriented approach by finite element (FE) and reduced basis (RB) methods. The main contribution of this paper is the “goal-oriented” proper orthogonal decomposition (POD)–Greedy sampling procedure within the RB approximation context. First, we introduce the RB recipe: Galerkin projection onto a space YN spanned by solutions of the governing PDE at N selected points in parameter space. This set of N parameter points is constructed by the standard POD–Greedy sampling procedure already developed. Second, based on the affine parameter dependence, we make use of the offline-online computational procedures: in the offline stage, we generate the RB space; in the online stage, given a new parameter value, we calculate rapidly and accurately the space-time RB output of interest and its associated asymptotic error. The proposed goal-oriented POD–Greedy sampling procedure can now be implemented and will look for the parameter points such that it minimizes this (asymptotic) output error rather than the solution error (or, error indicator which is the dual norm of residual) as in the standard POD–Greedy procedure. Numerical results show that the new goal-oriented POD–Greedy sampling procedure improves significantly the accuracy of the space-time output computation in comparison with the standard POD–Greedy one. The method is thus ideally suited for repeated, rapid and reliable evaluation of input-output relationships within the space-time setting. [less ▲]

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