There is a high interest in accelerating multiscale models using data-driven
surrogate modeling techniques. Creating a large training dataset encompassing
all relevant load scenarios is essential for a good surrogate, yet the
computational cost of producing this data quickly becomes a limiting factor.
Commonly, a pre-trained surrogate is used throughout the computational domain.
Here, we introduce an alternative adaptive mixture approach that uses a fast
probabilistic surrogate model as constitutive model when possible, but resorts
back to the true high-fidelity model when necessary. The surrogate is thus not
required to be accurate for every possible load condition, enabling a
significant reduction in the data collection time. We achieve this by creating
phases in the computational domain corresponding to the different models. These
phases evolve using a phase-field model driven by the surrogate uncertainty.
When the surrogate uncertainty becomes large, the phase-field model causes a
local transition from the surrogate to the high-fidelity model, maintaining a
highly accurate simulation. We discuss the requirements of this approach to
achieve accurate and numerically stable results and compare the phase-field
model to a purely local approach that does not enforce spatial smoothness for
the phase mixing. Using a Gaussian Process surrogate for an elasto-plastic
material, we demonstrate the potential of this mixture of models to accelerate
multiscale simulations.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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