Research and Prototyping Study of an LLM-Based Chatbot for Electromagnetic Simulations

2025-11-21

AI Summary

This study addresses the high computational cost and steep learning curve associated with electromagnetic simulation modeling. We propose a conversational, LLM-driven automation framework for electromagnetic simulation, centered on Google Gemini 2.0 Flash as the reasoning engine and tightly integrated with Gmsh (geometry generation), GetDP (finite-element solver), and Python (workflow orchestration). The method enables users to specify physical configurations, boundary conditions, and solution objectives via natural language, automatically generating executable simulation scripts, solving 2D eddy-current problems, and delivering customized post-processing and concise result summaries. Key contributions include the first end-to-end, natural-language-to-simulation pipeline for electromagnetic analysis and seamless integration of domain-specific solvers with modern LLMs. Experimental evaluation across diverse conductor geometries demonstrates high modeling accuracy, strong generalization capability, and substantial reductions in user expertise requirements—thereby improving modeling efficiency and human–machine interaction.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Natural Language Processing: (Large) Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Reducing electromagnetic simulation setup time using generative AI
• Automating 2D finite element model generation with LLM chatbot
• Enabling custom post-processing and model summary capabilities

Innovation

• LLM chatbot automates electromagnetic simulation setup
• Generates 2D finite element models using Gmsh and GetDP
• Python coordinates workflow with custom post-processing capabilities