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Probabilistic learning and updating of a digital twin for composite material systems
Abstract : This paper presents an approach for characterizing and estimating statistical dependence between a large number of observables in a composite material system. Conditional regression is carried out using the estimated joint density function, permitting a systematic exploration of interdependence between fine scale and coarse observables that can be used for both prognosis and design of complex material systems. An example demonstrates the integration of experimental data with a computational database. The statistical approach is based on the probabilistic learning on manifolds recently developed by the authors. This approach leverages intrinsic structure detected through diffusion on graphs with projected stochastic differential equations to generate samples constrained to that structure.
Cited literature [22 references]
https://hal-upec-upem.archives-ouvertes.fr/hal-02640409
Contributor : Christian Soize Connect in order to contact the contributor
Submitted on : Saturday, June 6, 2020 - 11:19:20 AM
Last modification on : Wednesday, October 27, 2021 - 7:14:51 AM
Contributor : Christian Soize Connect in order to contact the contributor
Submitted on : Saturday, June 6, 2020 - 11:19:20 AM
Last modification on : Wednesday, October 27, 2021 - 7:14:51 AM
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