Reducing Experimental Testing in Space Propulsion Film Cooling Analyses by Pixelwise Generative Image Interpolation

arXiv:2605.29911v1 Announce Type: new Abstract: We propose a machine learning approach for image regression from sparse experimental measurements. We show the application of the proposed method on film cooling studies in propulsion system development, aiming to reduce the need for extensive physical testing. Our method employs a lightweight feed-forward neural network with positional encoding to generate images conditioned by input parameters. Validated on real and synthetic data, it achieves high image similarity (RMSE 93 %) while maintaining accuracy with a 30 \% reduction of measurements. W

Source: arXiv cs.LG — read the full report at the original publisher.

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