发表机构
Florida Institute of Technology（佛罗里达理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出物理引导的全卷积时空框架，直接多帧预测3D微观结构演化，结合Cahn-Hilliard残差正则化，实现高精度（结构相似性>0.97）和超30倍加速，无需额外推理成本。
AI 中文摘要
准确预测三维（3D）微观结构演化在计算上仍然要求很高，因为高保真相场模拟需要对大型体积域和长时间范围进行重复的数值积分。本研究开发了一种高效且可扩展的物理引导全卷积时空框架，用于直接多帧预测完整的3D微观结构序列。该模型结合了共享的3D空间编码和解码，以及一个分解的潜在转换器，该转换器整合了时间、局部3D空间和通道交互。在训练过程中加入离散的Cahn-Hilliard（CH）残差，以将学习到的演化正则化向控制动力学方向，而不改变推理路径。该框架在标称、长时程和减少时间上下文的预测下，对高分辨率3D旋节分解轨迹进行了评估。在完整时间上下文下，模型准确再现了体积演化，在标称预测范围内平均3D结构相似性保持在0.97以上。随着时间信息的减少，物理引导变得越来越有益，提高了预测鲁棒性和界面级形态的保持。该框架相对于参考谱相场求解器实现了超过30倍的墙钟加速，而物理引导不引入额外推理成本。这些结果确立了直接多帧、物理引导的全卷积学习作为密集3D相场动力学和重复微观结构预测的高通量替代策略。
英文摘要
Accurate prediction of three-dimensional (3D) microstructure evolution remains computationally demanding because high-fidelity phase-field simulations require repeated numerical integration over large volumetric domains and long temporal horizons. This study develops an efficient and scalable physics-guided fully convolutional spatiotemporal framework for direct multi-frame prediction of complete 3D microstructure sequences. The model combines shared 3D spatial encoding and decoding with a factorized latent translator that integrates temporal, local 3D spatial, and channel interactions. A discrete Cahn--Hilliard (CH) residual is incorporated during training to regularize the learned evolution toward the governing dynamics without altering the inference pathway. The framework is evaluated on high-resolution 3D spinodal-decomposition trajectories under nominal, long-horizon, and reduced-temporal-context forecasting. Under full temporal context, the model accurately reproduces volumetric evolution, with average 3D structural similarity remaining above 0.97 over the nominal prediction horizon. Physics guidance becomes increasingly beneficial as temporal information is reduced, improving predictive robustness and preservation of interface-level morphology. The framework also achieves more than a 30-fold wall-clock speedup relative to the reference spectral phase-field solver, while physics guidance introduces no additional inference cost. These results establish direct multi-frame, physics-guided fully convolutional learning as a high-throughput surrogate strategy for dense 3D phase-field dynamics and repeated microstructure forecasting.
