Surrogate Models for Stochastic Al-Cu Phase-Field Solidification

Abstract:Phase-field models resolve microstructural evolution during rapid alloy solidification, but their computational cost limits long trajectories, stochastic ensembles, and studies across process conditions. We present a variance-preserving denoising diffusion bridge model (VP-DDBM) for state-conditioned prediction of two-dimensional Al-Cu solidification. Given the current phase, concentration, and prescribed temperature fields, the model predicts the subsequent state and is applied autoregressively. Across two held-out thermal gradients, VP-DDBM produces lower long-horizon errors than deterministic U-Net and SI-ConvNeXt baselines in domain-mean phase, domain-mean Cu concentration, and leading-tip position. It also retains fine dendritic structure over long rollouts, whereas the deterministic U-Net progressively smooths the morphology. VP-DDBM predicts the phase and concentration evolution approximately $73\times$ faster than the phase-field solver, showing potential for faster process-condition screening and ensemble-based microstructure studies.

Accepted at the AI4Mat workshop at NeurIPS 2026. Includes supplementary material

Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci)

arXiv:2611.04695 [cs.LG]

(or arXiv:2611.04695v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2611.04695 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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