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This study leverages the Conditional Generative Pre-trained Transformer, specifically ChatGPT-4, to develop a Python-based application for analyzing the propagation of electromagnetic waves in dielectric media using the two-dimensional Finite-Difference Time-Domain (FDTD) approach. The primary aim is to examine how electromagnetic waves interact with various dielectric environments, focusing on their reflection, transmission, and absorption properties. This work aims to exploit the capabilities of ChatGPT-4 to craft an accurate simulation tool. The performance of the generated FDTD simulations by ChatGPT-4 is evaluated. The findings suggest that ChatGPT-4 successfully creates FDTD program codes that conform to expected physical outcomes, albeit with slight variances. This research underscores the potential and accuracy of AI in handling sophisticated electromagnetic simulation tasks.

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Electromagnetic Wave Propagation in Dielectric Media

  • 章节

  • 发表日期：

    2025年

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  • Tejas Viresh Anvekar,
  • Bernhard Eidel

摘要

This study leverages the Conditional Generative Pre-trained Transformer, specifically ChatGPT-4, to develop a Python-based application for analyzing the propagation of electromagnetic waves in dielectric media using the two-dimensional Finite-Difference Time-Domain (FDTD) approach. The primary aim is to examine how electromagnetic waves interact with various dielectric environments, focusing on their reflection, transmission, and absorption properties. This work aims to exploit the capabilities of ChatGPT-4 to craft an accurate simulation tool. The performance of the generated FDTD simulations by ChatGPT-4 is evaluated. The findings suggest that ChatGPT-4 successfully creates FDTD program codes that conform to expected physical outcomes, albeit with slight variances. This research underscores the potential and accuracy of AI in handling sophisticated electromagnetic simulation tasks.