Boundary-Decoder network for inverse prediction of capacitor electrostatic analysis

TL;DR

This paper introduces a boundary-decoder neural network that efficiently predicts electrostatic behavior of capacitors under changing boundary conditions, outperforming traditional and existing neural methods.

Contribution

The paper presents a novel boundary-decoder network that models boundary condition variations directly, reducing re-training needs and improving prediction accuracy in electrostatic simulations.

Findings

Outperforms vanilla neural networks and PINN in dynamic boundary scenarios.

Retains full forward modeling capabilities.

Significantly reduces computational time compared to traditional methods.

Abstract

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.