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

• URL: http://arxiv.org/abs/2511.17680v1
• Date: Fri, 21 Nov 2025 08:26:22 GMT
• Title: Research and Prototyping Study of an LLM-Based Chatbot for Electromagnetic Simulations
• Authors: Albert Piwonski, Mirsad Hadžiefendić,
• Abstract summary: This work addresses the question of how generative artificial intelligence can be used to reduce the time required to set up electromagnetic simulation models.<n>A large language model is presented, enabling the automated generation of simulation models with various functional enhancements.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• URDF-Anything: Constructing Articulated Objects with 3D Multimodal Language Model [76.08429266631823]
  We propose an end-to-end automatic reconstruction framework based on a 3D multimodal large language model (MLLM)<n>URDF-Anything utilizes an autoregressive prediction framework based on point-cloud and text multimodal input to jointly optimize geometric segmentation and kinematic parameter prediction.<n> Experiments on both simulated and real-world datasets demonstrate that our method significantly outperforms existing approaches.
  arXiv  Detail & Related papers  (2025-11-02T13:45:51Z)
• Automating MD simulations for Proteins using Large language Models: NAMD-Agent [9.339909188265333]
  We introduce an automated pipeline that leverages Large Language Models (LLMs), specifically Gemini 2.0 Flash, in conjunction with python scripting and Selenium based web automation.<n>The pipeline exploits CHARMM GUI's comprehensive web-based interface for preparing simulation-ready inputs for NAMD.<n>Results demonstrate that this approach reduces setup time, minimizes manual errors, and offers a scalable solution for handling multiple protein systems in parallel.
  arXiv  Detail & Related papers  (2025-07-10T16:17:40Z)
• DoMINO: A Decomposable Multi-scale Iterative Neural Operator for Modeling Large Scale Engineering Simulations [2.300471499347615]
  DoMINO is a point cloudbased machine learning model that uses local geometric information to predict flow fields on discrete points.<n>DoMINO is validated for the automotive aerodynamics use case using the DrivAerML dataset.
  arXiv  Detail & Related papers  (2025-01-23T03:28:10Z)
• LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins [4.773175285216063]
  This paper presents a novel framework that applies large language models (LLMs) to automate the parametrization of simulation models in digital twins. The proposed approach enhances the usability of simulation model by infusing it with knowledges from LLM. The system has the potential to increase user-friendliness and reduce the cognitive load on human users.
  arXiv  Detail & Related papers  (2024-05-28T11:59:40Z)
• DiffGen: Robot Demonstration Generation via Differentiable Physics Simulation, Differentiable Rendering, and Vision-Language Model [72.66465487508556]
  DiffGen is a novel framework that integrates differentiable physics simulation, differentiable rendering, and a vision-language model. It can generate realistic robot demonstrations by minimizing the distance between the embedding of the language instruction and the embedding of the simulated observation. Experiments demonstrate that with DiffGen, we could efficiently and effectively generate robot data with minimal human effort or training time.
  arXiv  Detail & Related papers  (2024-05-12T15:38:17Z)
• Visual Deformation Detection Using Soft Material Simulation for Pre-training of Condition Assessment Models [3.0477617036157136]
  It proposes using Blender, an open-source simulation tool, to create synthetic datasets for machine learning (ML) models.<n>The process involves translating expert information into shape key parameters to simulate deformations, generating images for both deformed and non-deformed objects.
  arXiv  Detail & Related papers  (2024-04-02T01:58:53Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter [65.93205328894608]
  We learn the residual errors between a dynamic and/or simulator model and the real robot. We show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
  arXiv  Detail & Related papers  (2022-09-07T15:15:12Z)
• MLMOD: Machine Learning Methods for Data-Driven Modeling in LAMMPS [0.0]
  We present a prototype C++/Python package for characterizing microscale mechanics and molecular dynamics. The package is integrated currently with the mesomod and molecular dynamics simulation package LAMMPS and PyTorch.
  arXiv  Detail & Related papers  (2021-07-29T22:55:26Z)
• Deep Imitation Learning for Bimanual Robotic Manipulation [70.56142804957187]
  We present a deep imitation learning framework for robotic bimanual manipulation. A core challenge is to generalize the manipulation skills to objects in different locations. We propose to (i) decompose the multi-modal dynamics into elemental movement primitives, (ii) parameterize each primitive using a recurrent graph neural network to capture interactions, and (iii) integrate a high-level planner that composes primitives sequentially and a low-level controller to combine primitive dynamics and inverse kinematics control.
  arXiv  Detail & Related papers  (2020-10-11T01:40:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.