EMSL groups material points into clusters, samples a reference strain per cluster once per increment, and computes a linearised stress estimate from the reference tangent and POD strain modes, yielding an affine reduced system that requires no iterations online and Pareto-dominates prior strain-cubc
Hern´ andez, M.A
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A two-level HPRNN framework is proposed that embeds physical properties into latent spaces to surrogate nonlinear elasto-plastic yarn behavior and meso-to-macro transitions for woven composites.
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Empirical Material Sampling and Linearisation -- A Simple and Efficient Strain-Space Model Order Reduction Approach for Computational Homogenisation in Large-Deformation Hyperelasticity
EMSL groups material points into clusters, samples a reference strain per cluster once per increment, and computes a linearised stress estimate from the reference tangent and POD strain modes, yielding an affine reduced system that requires no iterations online and Pareto-dominates prior strain-cubc
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Multiscale Analysis of Woven Composites Using Hierarchical Physically Recurrent Neural Networks
A two-level HPRNN framework is proposed that embeds physical properties into latent spaces to surrogate nonlinear elasto-plastic yarn behavior and meso-to-macro transitions for woven composites.