A manifold learning approach for integrated computational materials engineering
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Publication:1639584
DOI10.1007/s11831-016-9172-5zbMath1390.74196OpenAlexW2306490075WikidataQ113323214 ScholiaQ113323214MaRDI QIDQ1639584
Publication date: 13 June 2018
Published in: Archives of Computational Methods in Engineering (Search for Journal in Brave)
Full work available at URL: http://zaguan.unizar.es/record/63077
composite materialsmachine learningmanifold learningcomputational homogenizationmodel order reductionlocally linear embeddingreal time thermal simulation
Image analysis in multivariate analysis (62H35) Learning and adaptive systems in artificial intelligence (68T05) Applications of statistics to physics (62P35) Homogenization in equilibrium problems of solid mechanics (74Q05) Stochastic and other probabilistic methods applied to problems in solid mechanics (74S60)
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On a Physics-Compatible Approach for Data-Driven Computational Mechanics ⋮ Nonlinear shape-manifold learning approach: concepts, tools and applications ⋮ A manifold learning approach to data-driven computational elasticity and inelasticity ⋮ kPCA-based parametric solutions within the PGD framework ⋮ A nonlinear data-driven reduced order model for computational homogenization with physics/pattern-guided sampling ⋮ Data-driven elasto-(visco)-plasticity involving hidden state variables ⋮ Efficient multiscale modeling of heterogeneous materials using deep neural networks ⋮ Data-driven non-linear elasticity: constitutive manifold construction and problem discretization ⋮ Data-driven fracture mechanics ⋮ On the effect of phase transition on the manifold dimensionality: application to the Ising model ⋮ Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction ⋮ Homogenization of transport properties of composites based on stochastic dynamics
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