mlsanityData to 7 Oct 2026

AutoSiMP: Autonomous Topology Optimization from Natural Language via LLM-Driven Problem Configuration and Adaptive Solver Control

arXiv 2603.27000 ↗First posted 27 Mar 2026cs.CE cs.AIcited by 0

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Abstract (arXiv)

We present AutoSiMP, an autonomous pipeline that transforms a natural-language structural problem description into a validated, binary topology without manual configuration. The pipeline comprises five modules: (1) an LLM-based configurator that parses a plain-English prompt into a validated specification of geometry, supports, loads, passive regions, and mesh parameters; (2) a boundary-condition generator producing solver-ready DOF arrays, force vectors, and passive-element masks; (3) a three-field SIMP solver with Heaviside projection and pluggable continuation control; (4) an eight-check structural evaluator (connectivity, compliance, grayness, volume fraction, convergence, plus three informational quality metrics); and (5) a closed-loop retry mechanism. We evaluate on three axes. Configuration accuracy: across 10 diverse problems the configurator produces valid specifications on all cases with a median compliance penalty of +0.3% versus expert ground truth. Controller comparison: on 17 benchmarks with six controllers sharing an identical sharpening tail, the LLM controller achieves the lowest median compliance but 76.5% pass rate, while the deterministic schedule achieves 100% pass rate at only +1.5% higher compliance. End-to-end reliability: with the schedule controller, all LLM-configured problems pass every quality check on the first attempt - no retries needed. Among the systems surveyed in this work (Table 1), AutoSiMP is the first to close the full loop from natural-language problem description to validated structural topology. The complete codebase, all specifications, and an interactive web demo will be released upon journal acceptance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 67 references that are in the map. First posted on arXiv 27 Mar 2026.

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30 of 67 references.

References · 30

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18 more
  1. 2024Evolutionary Computation in the Era of Large Language Model: Survey and Roadmapcited by 89
  2. 2022Diffusion Models Beat GANs on Topology Optimizationcited by 79
  3. 2020TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domaincited by 65
  4. 2018Deep learning for determining a near-optimal topological design without any iterationcited by 40
  5. 2017Neural networks for topology optimizationcited by 39
  6. 2022Automated Dynamic Algorithm Configurationcited by 37
  7. 2022On the use of Artificial Neural Networks in Topology Optimisationcited by 35
  8. 20183D Topology Optimization using Convolutional Neural Networkscited by 27
  9. 2022Topology Optimization via Machine Learning and Deep Learning: A Reviewcited by 22
  10. 2020Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designscited by 19
  11. 2025Integrating Large Language Models for Automated Structural Analysiscited by 12
  12. 2020Self-Directed Online Machine Learning for Topology Optimizationcited by 10
  13. 2025FeaGPT: an End-to-End agentic-AI for Finite Element Analysiscited by 10
  14. 2023Large Language and Text-to-3D Models for Engineering Design Optimizationcited by 7
  15. 2024Using Large Language Models for Parametric Shape Optimizationcited by 7
  16. 2020A new generation 99 line Matlab code for compliance Topology Optimization and its extension to 3Dcited by 5
  17. 2025Transformer-based Topology Optimizationcited by 1
  18. 2026Large Language Models as Optimization Controllers: Adaptive Continuation for SIMP Topology Optimizationcited by 1

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