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Discover how encoder-decoder CNNs serve as efficient surrogates for diffusion solvers, improving computational speed and model performance.
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The abstract discusses the utilization of encoder-decoder CNN architectures as surrogates for steady-state diffusion solvers. It explores the impact of factors like training set size, loss functions, and hyperparameters on model performance, highlighting the challenges and opportunities in developing deep learning surrogates for diffusion problems.