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Solid propellant grain reverse design via generative deep learning
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DOI：10.1016/j.ast.2025.110496.png)
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Abstract
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• Grain reverse design can identify optimal grain geometries that match the desired motor performance curves (e.g., pressure-time curves). • The deep neural field with a conditional auto-decoder successfully extracts features from grain shapes. • The latent denoising diffusion probabilistic model effectively generates 2D grain shapes in a high-dimensional design space.
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5.8
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1.0W
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3.0W
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