Research and Prototyping Study of an LLM-Based Chatbot for Electromagnetic Simulations
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Abstract (arXiv)
This work addresses the question of how generative artificial intelligence can be used to reduce the time required to set up electromagnetic simulation models. A chatbot based on a large language model is presented, enabling the automated generation of simulation models with various functional enhancements. A chatbot-driven workflow based on the large language model Google Gemini 2.0 Flash automatically generates and solves two-dimensional finite element eddy current models using Gmsh and GetDP. Python is used to coordinate and automate interactions between the workflow components. The study considers conductor geometries with circular cross-sections of variable position and number. Additionally, users can define custom post-processing routines and receive a concise summary of model information and simulation results. Each functional enhancement includes the corresponding architectural modifications and illustrative case studies.
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References · 6
- 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
- 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 2,018
- 2019DeepXDE: A deep learning library for solving differential equationscited by 619
- 2017Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equationscited by 208
- 2025On the Theoretical Limitations of Embedding-Based Retrievalcited by 104
- 2025A multi-agentic framework for real-time, autonomous freeform metasurface designcited by 14
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