  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.
