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This paper introduces a feed-forward neural network with positional encoding for pixelwise image regression, conditioned on input parameters, to reduce experimental testing in space propulsion film cooling analyses. The method is validated on both real and synthetic film cooling data, demonstrating high image similarity (RMSE < 8%, SSIM > 93%) while reducing measurement requirements by 30%. A knowledge-informed extension further enhances local adaptability of the generated images.
Cut your film cooling experiments by 30% without sacrificing image quality using this lightweight neural network for pixelwise image interpolation.
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<8 %, SSIM>93 %) while maintaining accuracy with a 30 \% reduction of measurements. We further propose a knowledge-informed extension for local adaptability of the generated images. This approach significantly reduces required tests while preserving high-quality data, enabling efficient optimization of coolant injector configurations with applications beyond aerospace.