Engineering.ai: A Platform for Teams of AI Engineers in Computational Design
Ran Xu, Yupeng Qi, Jingsen Feng, Xu Chu

TL;DR
Engineering.ai introduces a hierarchical multi-agent platform that enables autonomous teams of AI engineers to perform complex computational design tasks efficiently, validated by UAV wing optimization with perfect success rate.
Contribution
This work presents a novel multi-agent AI framework for computational design, integrating domain-specific LLMs, data management, and multidisciplinary simulation tools, advancing autonomous engineering workflows.
Findings
100% success rate across 400+ configurations
Zero mesh generation failures or solver issues
Autonomous multi-agent collaboration ensures reliable results
Abstract
In modern engineering practice, human engineers collaborate in specialized teams to design complex products, with each expert completing their respective tasks while communicating and exchanging results and data with one another. While this division of expertise is essential for managing multidisciplinary complexity, it demands substantial development time and cost. Recently, we introduced OpenFOAMGPT (1.0, 2.0), which functions as an autonomous AI engineer for computational fluid dynamics, and turbulence.ai, which can conduct end-to-end research in fluid mechanics draft publications and PhD theses. Building upon these foundations, we present Engineering.ai, a platform for teams of AI engineers in computational design. The framework employs a hierarchical multi-agent architecture where a Chief Engineer coordinates specialized agents consisting of Aerodynamics, Structural, Acoustic, and…
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Taxonomy
TopicsLattice Boltzmann Simulation Studies · Model Reduction and Neural Networks · Scientific Computing and Data Management
