Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN)
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Publication:2160403
DOI10.1016/j.cma.2022.115190OpenAlexW4283067940MaRDI QIDQ2160403
Ioannis Stefanou, Filippo Masi
Publication date: 3 August 2022
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2108.13137
Related Items (12)
Learning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental Data ⋮ Advanced discretization techniques for hyperelastic physics-augmented neural networks ⋮ Integrated finite element neural network (I-FENN) for non-local continuum damage mechanics ⋮ A new family of constitutive artificial neural networks towards automated model discovery ⋮ A comparative study on different neural network architectures to model inelasticity ⋮ Automated model discovery for skin: discovering the best model, data, and experiment ⋮ Two-stage 2D-to-3d reconstruction of realistic microstructures: implementation and numerical validation by effective properties ⋮ \(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining ⋮ Physically informed deep homogenization neural network for unidirectional multiphase/multi-inclusion thermoconductive composites ⋮ Incremental neural controlled differential equations for modeling of path-dependent material behavior ⋮ Neural integration for constitutive equations using small data ⋮ Physically recurrent neural networks for path-dependent heterogeneous materials: embedding constitutive models in a data-driven surrogate
Uses Software
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