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In the thermal-condition time-series prediction framework, the grain size distribution is encoded by a convolutional neural network (CNN), thermal descriptors are embedded via a dedicated multilayer perceptron (MLP), and the temporal evolution prediction is performed by a recurrent network.
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11/5/2026
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UnverifiedThe research team's previous work had demonstrated that under isothermal conditions, a recurrent neural network (RNN) can be used to treat the grain size distribution as a time series for prediction, generating a complete grain growth trajectory within seconds without running expensive partial differential equation (PDE) simulations.75% similarUnverifiedThe framework in this paper supports calibrators in multiple forms, ranging from temperature scaling and matrix scaling to neural network layers.69% similarVerifiedAI weather models train deep neural networks on massive historical meteorological reanalysis data, achieving inference speeds hundreds to thousands of times faster than traditional NWP.68% similarVerifiedTraditional classification models can improve calibration through post-processing methods such as Temperature Scaling.67% similarUnverifiedIn the parameter spectrum of AI programming tools, temperature controls output randomness, context window determines the amount of visible information, and effort controls depth of thinking.65% similar
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