Measuring stress fields in fluids and soft materials is crucial in various
fields such as mechanical engineering, medicine, and bioengineering. Cependant,
conventional methods that calculate stress fields from velocity fields struggle
to measure complex fluids where the stress constitutive equation is unknown. À
résoudre ce problème, we propose a novel approach that combines photoelastic
mesures — which can non-invasively visualize internal stresses — avec
machine learning to measure stress fields. The machine learning model, que nous
named physics-informed convolutional encoder-decoder (PICED), integrates a
convolutional neural network (CNN)-based encoder-decoder model with a
physics-informed neural network (PINN). Using this approach, tridimensionnel
stress fields can be predicted with high accuracy for multiple interpolated
data points in a rectangular channel flow.

Cet article explore les excursions dans le temps et leurs implications.

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