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karelmatous's picture

A nonlinear data-driven reduced order model for computational homogenization with physics/pattern-guided sampling

Developing an accurate nonlinear reduced order model from simulation data has been an outstanding research topic for many years. For many physical systems, data collection is very expensive and the optimal data distribution is not known in advance. Thus, maximizing the information gain remains a grand challenge. In a recent paper, Bhattacharjee and Matous (2016) proposed a manifold-based nonlinear reduced order model for multiscale problems in mechanics of materials. Expanding this work here, we develop a novel sampling strategy based on the physics/pattern-guided data distribution.

ludovicnoels's picture

PhD & Post-doc positions in the context of “Data-driven Multi-scale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials”

Context
As part of a collaborative FET-Open H2020 project between different universities and a SME, there exist open PhD and Post-Doc positions is the context of
• Experimental characterisation of SLS printed structures;
• Models development of SLS process and constitutive behaviours;
• Developments of homogenisation methods and surrogate models (e.g. machine learning etc.)

20 PhD students in Data-driven Computational Modelling

Dear colleagues, 

Luxembourg FNR is funding 20 PhD students 

https://driven.uni.lu/

Please contact the principal investigators. See attachment.

Regards,

Stéphane P.A. BORDAS

http://legato-team.eu/

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