<ul><li>A CNN and MLP are introduced to build a surrogate model for radiative heat transfer in 2-D furnaces with spectrally participative gases.</li><li>CNN architecture is adapted for the problem inputs, resulting in a significant speedup and accuracy compared to the classical solver.</li><li>The performance of CNN is compared to MLP in terms of speed, accuracy, and robustness to hyper-parameter changes.</li><li>Results show CNN outperforms MLP in precision and stability while providing a deeper understanding of model behavior with dataset size analysis.</li></ul>

Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases

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