{"ID":2838488,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17680","arxiv_id":"2511.17680","title":"Research and Prototyping Study of an LLM-Based Chatbot for Electromagnetic Simulations","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.","short_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-d...","url_abs":"https://arxiv.org/abs/2511.17680","url_pdf":"https://arxiv.org/pdf/2511.17680v2","authors":"[\"Albert Piwonski\",\"Mirsad Hadžiefendić\"]","published":"2025-11-21T08:26:22Z","proceeding":"cs.CE","tasks":"[\"cs.CE\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}