Adaptive goal-oriented data sampling in data-driven computational mechanics

DOI10.1016/j.cma.2023.115949OpenAlexW4323922649MaRDI QIDQ6101886

José E. Andrade, Laurent Stainier, Konstantinos Karapiperis, Michael Ortiz, Anna Gorgogianni

Publication date: 5 May 2023

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2139/ssrn.4334901

zbMATH Keywords

multiscale modelingdata-driven computingdata acquisition strategies

Mathematics Subject Classification ID

Mechanics of deformable solids (74-XX) Geophysics (86-XX)

Related Items (3)

Graph-based representation of history-dependent material response in the data-driven computational mechanics framework ⋮ Deep learning framework for multiscale finite element analysis based on data-driven mechanics and data augmentation ⋮ Data-driven games in computational mechanics

Cites Work

• Model-free data-driven inelasticity
• Model-free data-driven computational mechanics enhanced by tensor voting
• Data-driven fracture mechanics
• Constitutive artificial neural networks: a fast and general approach to predictive data-driven constitutive modeling by deep learning
• Efficient data structures for model-free data-driven computational mechanics
• Data-driven nonlocal mechanics: discovering the internal length scales of materials
• Data driven computing with noisy material data sets
• Data-based derivation of material response
• Data-driven computational mechanics
• A Micromechanical Description of Granular Material Behavior
• Numerical implementation of a neural network based material model in finite element analysis

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