The present article addresses an early-stage attempt on replacing the analyticity-based sink strength terms in rate equations by means of a surrogate representation. Here we emphasise, in the context of multiscale modelling, a combinative use of machine learning with scale analysis, through which a set of fine-resolution problems of partial differential equations describing the local sink behaviour can be asymptotically sorted out from the mean-field kinetics. Hence the training of machine learning formulation is restrictively oriented, that is, to express the local and already identified, but analytically unavailable nonlinear functional relationships between the sink strengths and other local continuum field quantities. Besides, the normally faster diffusion mechanisms on crystalline interfaces are particularly modelled by locally planar rate equations, and their linkages with rate equations for bulk diffusion are formulated by imposing an irregular boundary condition where point defect fluxes are discontinuous across the interfaces. Thus the distinctive role of crystalline interfaces as partial sinks and quick diffusion channels can be investigated. Methodologicalwise, the present treatment is also applicable for studying more complicated situation of long-term sink behaviour observed in irradiated materials.

本文介绍了通过代理表示形式替换速率方程中基于分析能力的下沉强度项的早期尝试。在这里，我们强调在多尺度建模的背景下，将机器学习与尺度分析结合起来使用，通过它可以从均值场动力学中渐进地分类出描述局部吸收行为的偏微分方程的一组精细分辨率问题。因此，机器学习公式的训练是限制性的，也就是说，要表达局部强度和已经识别出的但在沉陷强度与其他局部连续谱场量之间无法解析的非线性函数关系。除了，通常通过局部平面速率方程来模拟通常更快的晶体界面扩散机制，并通过施加不规则边界条件（其中点缺陷通量在界面上不连续）来建立它们与整体扩散速率方程的联系。因此，可以研究晶体界面作为部分凹陷和快速扩散通道的独特作用。从方法上讲，本治疗方法还适用于研究在辐照材料中观察到的长期沉陷行为的更复杂情况。因此，可以研究晶体界面作为部分凹陷和快速扩散通道的独特作用。从方法上讲，本治疗方法还适用于研究在辐照材料中观察到的长期沉陷行为的更复杂情况。因此，可以研究晶体界面作为部分凹陷和快速扩散通道的独特作用。从方法上讲，本治疗方法还适用于研究在辐照材料中观察到的长期沉陷行为的更复杂情况。