A computational framework for nanotrusses: input convex neural networks approach

DOI10.1016/j.euromechsol.2023.105195zbMath1530.74052arXiv2311.16715MaRDI QIDQ6181423

Domagoj Vrtovšnik, Neven Munjas, Valentina Košmerl, Marko Čanadija, Martin Zlatić

Publication date: 2 January 2024

Published in: European Journal of Mechanics. A. Solids (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2311.16715

zbMATH Keywords

finite element methodsingle-walled carbon nanotubemetamaterialsmall size effectmolecular dynamics-machine learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Finite element methods applied to problems in solid mechanics (74S05) Micromechanics of solids (74M25) Thin bodies, structures (74K99) Molecular, statistical, and kinetic theories in solid mechanics (74A25)

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• Dynamic stiffness of nonlocal damped nano-beams on elastic foundation
• A new family of constitutive artificial neural networks towards automated model discovery
• A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling

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