Phase-field simulations of liquid metal dealloying (LMD) can capture complex microstructural evolutions but can be prohibitively expensive for large domains and long time horizons. In this paper, we introduce a fully convolutional, conditionally parameterized U-Net surrogate designed to extrapolate far beyond its training data in both space and time. The architecture integrates convolutional self-attention, physically informed padding, and a flood-fill corrector method to maintain accuracy under extreme extrapolation, while conditioning on simulation parameters allows for flexible time-step skipping and adaptation to varying alloy compositions. To remove the need for costly solver-based initialization, we couple the surrogate with a conditional diffusion model that generates synthetic, physically consistent initial conditions. We train our surrogate on simulations generated over small domain sizes and short time spans, but, by taking advantage of the convolutional nature of U-Nets, we are able to run and extrapolate surrogate simulations for longer time horizons than what would be achievable with classic numerical solvers. Across multiple alloy compositions, the framework is able to reproduce the LMD physics accurately. It predicts key quantities of interest and spatial statistics with relative errors typically below 5% in the training regime and under 15% during large-scale, long time-horizon extrapolations. Our framework can also deliver speed-ups of up to 36,000 times, bringing the time to run weeks-long simulations down to a few seconds. This work is a first stepping stone towards high-fidelity extrapolation in both space and time of phase-field simulation for LMD.

翻译：液态金属脱合金的相场模拟能够捕捉复杂的微观结构演化，但对于大尺寸域和长时间尺度而言，其计算成本可能过高。本文提出了一种全卷积、条件参数化的U-Net代理模型，旨在实现远超训练数据范围的时空外推。该架构集成了卷积自注意力、物理信息填充和漫水填充校正方法，以在极端外推条件下保持精度，同时通过对模拟参数进行条件化，实现了灵活的时间步跳跃以及对不同合金成分的适应。为消除对基于求解器的高成本初始化的依赖，我们将代理模型与一个条件扩散模型耦合，后者能够生成合成且物理一致的初始条件。我们使用小尺寸域和短时间跨度生成的模拟数据训练代理模型，但通过利用U-Net的卷积特性，我们能够运行并外推代理模拟至比经典数值求解器所能达到的更长时间尺度。在多种合金成分下，该框架均能准确复现液态金属脱合金的物理过程。对于关键关注量和空间统计量的预测，其在训练范围内的相对误差通常低于5%，在大尺度、长时间外推中亦低于15%。该框架还能实现高达36,000倍的加速，将原本需数周运行的模拟缩短至数秒。本工作是实现液态金属脱合金相场模拟高保真时空外推的首个基石。