Finite element simulations are run by package design engineers to model design structures. The process is irreversible meaning every minute structural adjustment requires a fresh input parameter run. In this paper, the problem of modeling changing (small) design structures through varying input parameters is known as inverse prediction. We demonstrate inverse prediction on the electrostatics field of an air-filled capacitor dataset where the structural change is affected by a dynamic parameter to the boundary condition. Using recent AI such as deep generative model, we outperformed best baseline on inverse prediction both visually and in terms of quantitative measure.

翻译：封装设计工程师通过运行有限元仿真来建模设计结构。该过程不可逆，意味着任何细微的结构调整都需要重新运行输入参数。本文中，通过变化输入参数来建模（微小）设计结构变化的问题被称为逆向预测。我们在空气填充电容器数据集的静电场问题上展示了逆向预测，其中结构变化受边界条件动态参数的影响。通过采用深度生成模型等前沿人工智能技术，我们在逆向预测任务上实现了视觉与量化指标双重超越的最佳基线表现。