Measuring stress fields in fluids and soft materials is crucial in various
fields such as mechanical engineering, medicine, and bioengineering. Jedoch,
conventional methods that calculate stress fields from velocity fields struggle
to measure complex fluids where the stress constitutive equation is unknown. To
address this, we propose a novel approach that combines photoelastic
measurements — which can non-invasively visualize internal stresses — with
machine learning to measure stress fields. The machine learning model, which we
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, three-dimensional
stress fields can be predicted with high accuracy for multiple interpolated
data points in a rectangular channel flow.

Dieser Artikel untersucht Zeitreisen und deren Auswirkungen.

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