# Conformal mapping based Physics-informed neural networks for designing neutral inclusions

Source: [Conformal mapping based Physics-informed neural networks for designing neutral inclusions](<https://www.sota2.com/research/paper/68fa5cd31e209131ce9d480c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-04T05:00:24.095Z

Authors: Daehee Cho, Hyeonmin Yun, Jaeyong Lee, Mikyoung Lim

Published: 2025-01-14T03:20:17.000Z

## Abstract

We address the neutral inclusion problem with imperfect boundary conditions, focusing on designing interface functions for inclusions of arbitrary shapes. Traditional Physics-Informed Neural Networks (PINNs) struggle with this inverse problem, leading to the development of Conformal Mapping Coordinates Physics-Informed Neural Networks (CoCo-PINNs), which integrate geometric function theory with PINNs. CoCo-PINNs effectively solve forward-inverse problems by modeling the interface function through neural network training, which yields a neutral inclusion effect. This approach enhances the performance of PINNs in terms of credibility, consistency, and stability.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.07809>)

[PDF](<https://arxiv.org/pdf/2501.07809.pdf>)