用深度学习快速预测电容边界变化下的静电场，比传统方法快且无需重训练。
Boundary-Decoder network for inverse prediction of capacitor electrostatic analysis
- 设计边界解码网络，端到端建模边界条件变化对静电场的影响。
- 在电容结构上测试，动态边界条件下性能显著优于NN和PINN。
- 既可做快速逆向预测，也可作为通用前向模型，适合仿真加速场景。
传统静电仿真基于网格，将偏微分方程转化为代数系统，通过数值方法近似求解，耗时且边界条件变化需重新计算。新兴的物理信息神经网络（PINN）同样在边界条件改变时需重新训练。本文提出一种端到端深度学习方法，直接建模边界条件参数变化对静电场的影响。该方法在长空气电容器结构的测试问题上进行了验证，与普通神经网络（NN）和PINN对比显示，在动态边界条件下表现更优，同时保持作为前向模型的完整能力。
原文摘要 · Abstract (English)
Traditional electrostatic simulation are meshed-based methods which convert partial differential equations into an algebraic system of equations and their solutions are approximated through numerical methods. These methods are time consuming and any changes in their initial or boundary conditions will require solving the numerical problem again. Newer computational methods such as the physics informed neural net (PINN) similarly require re-training when boundary conditions changes. In this work, we propose an end-to-end deep learning approach to model parameter changes to the boundary conditions. The proposed method is demonstrated on the test problem of a long air-filled capacitor structure. The proposed approach is compared to plain vanilla deep learning (NN) and PINN. It is shown that our method can significantly outperform both NN and PINN under dynamic boundary condition as well as retaining its full capability as a forward model.
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