A Multidisciplinary Design and Optimization (MDO) Agent Driven by Large Language Models

• URL: http://arxiv.org/abs/2511.17511v1
• Date: Mon, 06 Oct 2025 01:26:55 GMT
• Title: A Multidisciplinary Design and Optimization (MDO) Agent Driven by Large Language Models
• Authors: Bingkun Guo, Wentian Li, Xiaojian Liu, Jiaqi Luo, Zibin Yu, Dalong Dong, Shuyou Zhang, Yiming Zhang,
• Abstract summary: We present a Multidisciplinary Design and Optimization (MDO) Agent driven by Large Language Models (LLMs)<n>The agent semi-automates the end-to-end workflow by orchestrating three core capabilities: (i) natural-language-driven parametric modeling, (ii) retrieval-augmented generation (RAG) for knowledge-grounded conceptualization, and (iii) intelligent orchestration of engineering software for performance verification and optimization.
• Score: 7.509742996975501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To accelerate mechanical design and enhance design quality and innovation, we present a Multidisciplinary Design and Optimization (MDO) Agent driven by Large Language Models (LLMs). The agent semi-automates the end-to-end workflow by orchestrating three core capabilities: (i) natural-language-driven parametric modeling, (ii) retrieval-augmented generation (RAG) for knowledge-grounded conceptualization, and (iii) intelligent orchestration of engineering software for performance verification and optimization. Working in tandem, these capabilities interpret high-level, unstructured intent, translate it into structured design representations, automatically construct parametric 3D CAD models, generate reliable concept variants using external knowledge bases, and conduct evaluation with iterative optimization via tool calls such as finite-element analysis (FEA). Validation on three representative cases - a gas-turbine blade, a machine-tool column, and a fractal heat sink - shows that the agent completes the pipeline from natural-language intent to verified and optimized designs with reduced manual scripting and setup effort, while promoting innovative design exploration. This work points to a practical path toward human-AI collaborative mechanical engineering and lays a foundation for more dependable, vertically customized MDO systems.

Related papers

• CeProAgents: A Hierarchical Agents System for Automated Chemical Process Development [73.33844908703799]
  CeProAgents is a hierarchical multi-agent system designed to automate the development of chemical process through collaborative division of labor.<n>To rigorously evaluate the system, we establish CeProBench, a multi-dimensional benchmark structured around three core pillars of chemical engineering.
  arXiv  Detail & Related papers  (2026-03-02T09:37:18Z)
• EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
  Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
  arXiv  Detail & Related papers  (2026-01-29T11:33:49Z)
• From Idea to Co-Creation: A Planner-Actor-Critic Framework for Agent Augmented 3D Modeling [13.750761293338854]
  We present a framework that extends the Actor-Critic architecture to creative 3D modeling through multi-agent self-reflection and human-in-the-loop supervision.<n>In this design, the Planner coordinates modeling steps, the Actor executes them, and the Critic provides iterative feedback, while human users act as supervisors and advisors throughout the process.
  arXiv  Detail & Related papers  (2026-01-08T15:18:12Z)
• AgentMath: Empowering Mathematical Reasoning for Large Language Models via Tool-Augmented Agent [80.83250816918861]
  Large Reasoning Models (LRMs) like o3 and DeepSeek-R1 have achieved remarkable progress in natural language reasoning with long chain-of-thought.<n>However, they remain computationally inefficient and struggle with accuracy when solving problems requiring complex mathematical operations.<n>We present AgentMath, an agent framework that seamlessly integrates language models' reasoning capabilities with code interpreters' computational precision.
  arXiv  Detail & Related papers  (2025-12-23T19:57:49Z)
• TIGeR: Tool-Integrated Geometric Reasoning in Vision-Language Models for Robotics [53.442362491589726]
  We present TIGeR (Tool-Integrated Geometric Reasoning), a novel framework that transforms Vision-Language Models (VLMs) into geometric computers.<n>Rather than attempting to internalize complex geometric operations within neural networks, TIGeR empowers models to recognize geometric reasoning requirements.<n>We show that TIGeR achieves SOTA performance on geometric reasoning benchmarks while demonstrating centimeter-level precision in real-world robotic manipulation tasks.
  arXiv  Detail & Related papers  (2025-10-08T16:20:23Z)
• Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments [70.42705564227548]
  We propose an automated environment construction pipeline for large language models (LLMs)<n>This enables the creation of high-quality training environments that provide detailed and measurable feedback without relying on external tools.<n>We also introduce a verifiable reward mechanism that evaluates both the precision of tool use and the completeness of task execution.
  arXiv  Detail & Related papers  (2025-08-12T09:45:19Z)
• An Agentic Framework for Autonomous Metamaterial Modeling and Inverse Design [2.66269503676104]
  We develop and demonstrate a framework specifically for the inverse design of photonic metamaterials.<n>The framework's effectiveness is demonstrated in its ability to automate, reason, plan, and adapt.<n> Notably, the Agentic Framework possesses internal reflection and decision flexibility, permitting highly varied and potentially novel outputs.
  arXiv  Detail & Related papers  (2025-06-07T22:10:05Z)
• An LLM-enabled Multi-Agent Autonomous Mechatronics Design Framework [49.633199780510864]
  This work proposes a multi-agent autonomous mechatronics design framework, integrating expertise across mechanical design, optimization, electronics, and software engineering.<n> operating primarily through a language-driven workflow, the framework incorporates structured human feedback to ensure robust performance under real-world constraints.<n>A fully functional autonomous vessel was developed with optimized propulsion, cost-effective electronics, and advanced control.
  arXiv  Detail & Related papers  (2025-04-20T16:57:45Z)
• AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design [24.258618104493532]
  We introduce the concept of "Design Agents" for engineering applications, particularly focusing on the automotive design process.<n>Our framework integrates AI-driven design agents into the traditional engineering workflow to augment creativity, enhance efficiency, and significantly accelerate the overall design cycle.
  arXiv  Detail & Related papers  (2025-03-30T04:57:17Z)
• Cliqueformer: Model-Based Optimization with Structured Transformers [102.55764949282906]
  Large neural networks excel at prediction tasks, but their application to design problems, such as protein engineering or materials discovery, requires solving offline model-based optimization (MBO) problems.<n>We present Cliqueformer, a transformer-based architecture that learns the black-box function's structure through functional graphical models (FGM)<n>Across various domains, including chemical and genetic design tasks, Cliqueformer demonstrates superior performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-10-17T00:35:47Z)
• Large Language Model Agent as a Mechanical Designer [7.136205674624813]
  We propose a framework that leverages a pretrained Large Language Model (LLM) in conjunction with an FEM module to autonomously generate, evaluate, and refine structural designs.<n>LLM operates without domain-specific fine-tuning, using general reasoning to propose design candidates, interpret FEM-derived performance metrics, and apply structurally sound modifications.<n>Compared to Non- Sorting Genetic Algorithm II (NSGA-II), our method achieves faster convergence and fewer FEM evaluations.
  arXiv  Detail & Related papers  (2024-04-26T16:41:24Z)
• Large Language and Text-to-3D Models for Engineering Design Optimization [0.1740313383876245]
  We study the potential of deep text-to-3D models in the engineering domain. We use Shap-E, a text-to-3D asset network by OpenAI, in the context of aerodynamic vehicle optimization.
  arXiv  Detail & Related papers  (2023-07-03T07:54:09Z)
• Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
  This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE) The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
  arXiv  Detail & Related papers  (2022-11-29T17:28:31Z)

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.