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Radermecker Arnaud

Département d'aérospatiale et mécanique > LTAS-Mécanique numérique non linéaire

A&M

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ORCID
0009-0001-1611-1683
Main Referenced Co-authors
Ponthot, Jean-Philippe  (1)
Ruess, Jean-Sébastien (1)
Main Referenced Keywords
deep learning (2); Bird strike (1); FE2 (1); finite element square (1); Finite element squared (1);
Main Referenced Unit & Research Centers
A&M - Aérospatiale et Mécanique - ULiège (1)
Main Referenced Disciplines
Aerospace & aeronautics engineering (5)

Publications (total 5)

The most downloaded
53 downloads
Radermecker, A. (14 May 2025). Accelerating large-strain multiscale simulations through deep learning [Poster presentation]. CMCS 2025 Computational Modeling of Complex Materials Across the scales. https://hdl.handle.net/2268/334083

The most cited

1 citations (Scopus® w/o selfcit.)

Radermecker, A., Ruess, J.-S., & Ponthot, J.-P. (2024). Towards a novel solid constitutive model for simulating gelatin impacts as a bird strike surrogate. Computational Particle Mechanics. doi:10.1007/s40571-024-00886-w https://hdl.handle.net/2268/325346

Radermecker, A. (02 July 2025). Efficient multiscale simulations of additively manufactured alloys at finite strain: Towards a hybrid approach combining FE-NN and FE2 [Paper presentation]. CM3P - Computational Methods for Multi-scale, Multi-uncertainty and Multi-physics Problems, Porto, Portugal.

Radermecker, A. (14 May 2025). Accelerating large-strain multiscale simulations through deep learning [Poster presentation]. CMCS 2025 Computational Modeling of Complex Materials Across the scales.

Radermecker, A. (07 February 2025). Multiscale Thermomechanical Simulations of 3D Printed Materials using Deep Learning [Poster presentation]. SPACE4RELAUCNH PhD Day, Bruxelles, Belgium.

Radermecker, A., Ruess, J.-S., & Ponthot, J.-P. (2024). Towards a novel solid constitutive model for simulating gelatin impacts as a bird strike surrogate. Computational Particle Mechanics. doi:10.1007/s40571-024-00886-w
Peer reviewed

Radermecker, A. (07 February 2024). Multiscale Thermomechanical Simulations of 3D Printed Materials using Deep Learning [Poster presentation]. SPACE4RELAUCNH PhD Day, Louvain-La-neuve, Belgium.

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