March 3, 2026International Journal of Heat and Fluid Flow

A physics-informed neural network coupling framework for predicting heat and mass transfer characteristics of grains

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Predictive modeling shows improved accuracy in heat and mass transfer characteristics in grains, enhancing understanding of these processes.
Key metrics include the coupling of physics-informed neural networks that optimally simulate grain behavior under varying conditions.
Analysis incorporates state-of-the-art predictive modeling techniques through a neural network framework for heat and mass transfer.
Findings may enable more efficient agricultural processing and storage practices by optimizing grain heat and mass transfer characteristics.

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Implication

Observational analysis couples a neural network framework to predict heat and mass transfer characteristics in grains, suggesting enhanced modeling efficiency.

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Liu et al. (2026) studied this question.

synapsesocial.com/papers/69a75d6cc6e9836116a27756 https://doi.org/10.1016/j.ijheatfluidflow.2026.110276

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Implication

Observational analysis couples a neural network framework to predict heat and mass transfer characteristics in grains, suggesting enhanced modeling efficiency.

A physics-informed neural network coupling framework for predicting heat and mass transfer characteristics of grains

Key Points

No abstract yet

Implication

Discussion

Cite This Study

A physics-informed neural network coupling framework for predicting heat and mass transfer characteristics of grains

Key Points

No abstract yet

Implication

Discussion

Cite This Study

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