Probabilistic learning and updating of a digital twin for composite material systems

Roger Ghanem (1) , Christian Soize (2) , Loujaine Mehrez (1) , Venkat Aitharaju (3)

1 USC - University of Southern California
2 MSME - Laboratoire de Modélisation et Simulation Multi Echelle
3 General Motors [Warren]

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.

Mots clés: Probabilistic learning; Digital twin; Composite material

Domaines: Machine Learning [stat.ML]; Applications [stat.AP]; Probabilités [math.PR]; Mécanique [physics]; Sciences de l'ingénieur [physics]

Roger Ghanem, Christian Soize, Loujaine Mehrez, Venkat Aitharaju. Probabilistic learning and updating of a digital twin for composite material systems. International Journal for Numerical Methods in Engineering, 2022, 123 (13), pp.3004-3020. ⟨10.1002/nme.6430⟩. ⟨hal-02640409⟩