TO-Master: an LLM-agent framework for automated topology optimization

2026-07-02

AI Summary

Although topology optimization has matured, its reliance on manual intervention—such as modeling, meshing, and boundary condition specification—hinders accessibility for non-experts. This work proposes the first conversational framework based on a large language model (LLM) agent that enables end-to-end topology optimization through natural language instructions and optional inputs (e.g., images, geometry, or meshes), automatically invoking finite element solvers and optimization tools. The approach integrates multi-load structural and thermal optimization, handles stress constraints, and employs few-shot prompting strategies, successfully reproducing benchmark cases while solving complex engineering problems and autonomously generating optimized structures, field distributions, and convergence curves. Ablation studies confirm that prompt design critically enhances system robustness, substantially lowering the usability barrier without compromising numerical reliability.

Abstract

Topology optimization (TO) has become a mature computational design method, but using it still requires substantial manual effort in geometry preparation, mesh generation, boundary-condition assignment, solver setup, and postprocessing. This implementation barrier limits the use of TO outside expert workflows, even when differentiable finite element solvers are available. This work introduces TO-Master, a large language model (LLM) agent framework that turns finite-element-based TO into a conversational, tool-orchestrated workflow. From natural language instructions and optional mesh, geometry, or image inputs, the agent selects computational tools, constructs finite element TO models, checks meshes and boundary conditions, and launches sensitivity-based optimization with typed solver arguments. The framework supports generated and uploaded meshes, image-to-mesh conversion, 2D and 3D structural compliance minimization, thermal conduction, multiple load cases, stress-constrained optimization, and engineering geometries. Numerical experiments show that TO-Master can reproduce standard benchmark results and solve more complex engineering examples while returning optimized results, field distributions, convergence histories, and interactive artifacts without user-written code. An instruction ablation study further shows that tool-usage rules, internal reasoning guidance, and few-shot examples are critical for robust formulation under ambiguous user input. By combining LLM-agent orchestration with deterministic finite element and optimization tools, TO-Master removes the burden of trivial setup and routine model construction, lowers the modeling barrier of TO, and preserves a reliable numerical workflow. The TO-Master platform is available online at https://www.bohrium.com/en/apps/to-master.

Problem

• topology optimization
• implementation barrier
• manual effort
• computational design
• user accessibility

Innovation

• LLM agent
• topology optimization
• finite element method
• tool orchestration
• natural language interface