A physics-informed CNN encoder-decoder reconstructs 3D stress in a Newtonian channel flow from 2D photoelastic images, achieving roughly 1-5% relative error on interpolated flow rates, but its physics loss omits the pressure gradient.
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Reconstruction of three-dimensional fluid stress field via photoelasticity using physics-informed convolutional encoder-decoder
A physics-informed CNN encoder-decoder reconstructs 3D stress in a Newtonian channel flow from 2D photoelastic images, achieving roughly 1-5% relative error on interpolated flow rates, but its physics loss omits the pressure gradient.