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arXiv Machine Learning By Zeping Chen, Ani Aprahamian, Khachatur V. Manukyan, Tengfei Luo

Retrainable physics-integrated neural differentiable modeling of sintering across material systems

Read the original on arXiv Machine Learning →

Sinter-PiNDiff is a retrainable physics‑integrated neural differentiable framework that predicts density and grain‑size evolution during sintering. It uses two neural networks to learn densification and grain‑growth coefficients within coupled rate equations, and a smooth saturation factor to limit densification near theoretical density. When trained on published data for MgO, Al‑doped ZnO, and CaO‑doped ThO₂, the model achieved the lowest mean errors across twelve material‑metric comparisons compared to multilayer perceptron and residual network baselines, demonstrating the importance of density‑dependent kinetic feedback and providing uncertainty estimates via deep ensembles.

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