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（LMD）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，其计算成本往往过高。本文提出了一种全卷积、条件参数化的U-Net替代模型，旨在实现远超训练数据范围的时空外推。该架构融合了卷积自注意力机制、物理启发的填充策略以及漫水填充校正方法，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及对不同合金成分的适应性。为消除对高成本求解器初始化的依赖，我们将替代模型与一个条件扩散模型耦合，以生成合成且物理一致的初始条件。我们基于小尺寸域和短时间跨度的模拟数据训练替代模型，但借助U-Net的卷积特性，能够运行并进行比传统数值求解器更长时域的外推模拟。在多种合金成分下，该框架均能准确复现LMD物理过程，其预测的关键目标量与空间统计量在训练区间内相对误差通常低于5%，在大规模长时间外推中亦能保持在15%以下。该框架还能实现高达36,000倍的加速比，将原本需数周运行的模拟缩短至数秒。本工作为实现LMD相场模拟的高保真时空外推迈出了重要的第一步。