Solving mechanics problems using numerical methods requires comprehensive intelligent capability of retrieving relevant knowledge and theory, constructing and executing codes, analyzing the results, a task that has thus far mainly been reserved for humans. While emerging AI methods can provide effective approaches to solve end-to-end problems, for instance via the use of deep surrogate models or various data analytics strategies, they often lack physical intuition since knowledge is baked into the parametric complement through training, offering less flexibility when it comes to incorporating mathematical or physical insights. By leveraging diverse capabilities of multiple dynamically interacting large language models (LLMs), we can overcome the limitations of conventional approaches and develop a new class of physics-inspired generative machine learning platform, here referred to as MechAgents. A set of AI agents can solve mechanics tasks, here demonstrated for elasticity problems, via autonomous collaborations. A two-agent team can effectively write, execute and self-correct code, in order to apply finite element methods to solve classical elasticity problems in various flavors (different boundary conditions, domain geometries, meshes, small/finite deformation and linear/hyper-elastic constitutive laws, and others). For more complex tasks, we construct a larger group of agents with enhanced division of labor among planning, formulating, coding, executing and criticizing the process and results. The agents mutually correct each other to improve the overall team-work performance in understanding, formulating and validating the solution. Our framework shows the potential of synergizing the intelligence of language models, the reliability of physics-based modeling, and the dynamic collaborations among diverse agents, opening novel avenues for automation of solving engineering problems.

翻译：使用数值方法解决力学问题需要具备检索相关知识理论、构建并执行代码、分析结果等综合智能能力，这一任务迄今主要由人类承担。尽管新兴的人工智能方法（如通过深度代理模型或各种数据分析策略）能提供解决端到端问题的有效途径，但由于知识通过训练嵌入参数化补充中，这些方法往往缺乏物理直觉，在融入数学或物理洞察方面灵活性不足。通过利用多个动态交互的大语言模型的不同能力，我们可以克服传统方法的局限性，开发一类全新的物理启发式生成机器学习平台，即本文所称的MechAgents。一组人工智能智能体可通过自主协作解决力学任务，本文以弹性问题为例进行演示。由两个智能体组成的团队能够有效编写、执行并自我修正代码，从而应用有限元方法解决各种形式的经典弹性问题（不同边界条件、域几何形状、网格、小/有限变形、线/超弹性本构律等）。对于更复杂的任务，我们构建了更大规模的智能体群体，在规划、公式化、编码、执行以及批判过程与结果之间实现了更强的劳动分工。智能体之间相互纠错，以提升团队在理解、公式化及验证解决方案方面的整体协作表现。我们的框架展示了协同语言模型智能、基于物理建模的可靠性以及多样化智能体动态协作的潜力，为工程问题求解自动化开辟了新的途径。