Retrainable physics-integrated neural differentiable modeling of sintering across material systems
Sintering is widely used to manufacture ceramics, but coupled densification and grain growth, material-dependent kinetics, and sparse measurements complicate predictive modeling and process design. We present Sinter-PiNDiff, a retrainable physics-integrated neural differentiable framework for predicting density and grain-size evolution. Two neural networks learn densification and grain-growth coefficients within coupled rate equations, while a smooth saturation factor attenuates densification near theoretical density. The same governing structure, network architecture, and training procedure were fitted independently to published data for MgO, Al-doped ZnO, and CaO-doped ThO2. Tests at he...
| Authors | Zeping Chen, Ani Aprahamian, Khachatur V. Manukyan et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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