用纯卷积网络加速并外推液态金属脱合金模拟，快16000倍且精度高。
Extrapolating Phase-Field Simulations in Space and Time with Purely Convolutional Architectures
- 设计全卷积U-Net，结合自注意力与物理感知填充，支持时空外推。
- 训练仅用短时小域数据，预测长时大域结果相对误差<5%~10%。
- 适合材料模拟、多尺度建模研究者，显著提升仿真效率。
液态金属脱合金（LMD）的相场模型能精细刻画微结构演化，但对大域或长时间模拟难以处理。本文提出一种条件参数化的全卷积U-Net代理模型，在时空上均远超训练范围进行外推。模型融合卷积自注意力与物理感知填充，参数条件化实现可变时间步跳过及适配不同合金体系。尽管仅在短时小域模拟上训练，仍利用卷积平移不变性，将预测扩展至远超传统求解器的时长。在训练范围内相对误差通常低于5%，外推至更大域和更晚时刻也低于10%。该方法计算加速达16,000倍，将数周模拟压缩至秒级，标志着高保真、可扩展的LMD相场模型外推迈出关键一步。
原文摘要 · Abstract (English)
Phase-field models of liquid metal dealloying (LMD) can resolve rich microstructural dynamics but become intractable for large domains or long time horizons. We present a conditionally parameterized, fully convolutional U-Net surrogate that generalizes far beyond its training window in both space and time. The design integrates convolutional self-attention and physics-aware padding, while parameter conditioning enables variable time-step skipping and adaptation to diverse alloy systems. Although trained only on short, small-scale simulations, the surrogate exploits the translational invariance of convolutions to extend predictions to much longer horizons than traditional solvers. It accurately reproduces key LMD physics, with relative errors typically under 5% within the training regime and below 10% when extrapolating to larger domains and later times. The method accelerates computations by up to 16,000 times, cutting weeks of simulation down to seconds, and marks an early step toward scalable, high-fidelity extrapolation of LMD phase-field models.
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