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Titlebook: Continuum Models and Discrete Systems; CMDS-14, Paris, Fran François Willot,Justin Dirrenberger,Andrej V. Cher Conference proceedings 2024

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https://doi.org/10.1007/978-3-658-03275-3 single realization of the BVP is computationally expensive, so the training dataset comprises only a few points differing from big data approaches. The presented application contributes to three-dimensional stochastic homogenization of heterogeneous linear elastic media, specifically when the mesoscale and macroscale are not separated.
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https://doi.org/10.1007/978-3-030-56174-1ologically different optimal structures. The bounds for the energy and optimal structures are explicitly computed for three-material conducting and elastic composites (one material is void). However, a unified approach to deriving a set of constraints in the supporting fields is yet to be developed.
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,Probabilistic Learning Inference Constrained by an Uncertain Model and a Target: A General Method w single realization of the BVP is computationally expensive, so the training dataset comprises only a few points differing from big data approaches. The presented application contributes to three-dimensional stochastic homogenization of heterogeneous linear elastic media, specifically when the mesoscale and macroscale are not separated.
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