Engineering.ai: A Platform for Teams of AI Engineers in Computational Design
🤖 AI Summary
To address high communication overhead and prolonged development cycles arising from disciplinary specialization in multidisciplinary collaborative design, this paper proposes a computational design platform tailored for AI engineering teams. The platform employs a hierarchical multi-agent architecture, wherein a Lead Engineer agent orchestrates domain-specific agents—Aerodynamics, Structures, Acoustics, and Optimization—using file-based mediation to ensure data traceability and reproducibility, and integrates a persistent memory system to enable end-to-end autonomous decision-making. Each agent is powered by large language models and natively interfaces with multiphysics simulation tools—including FreeCAD, Gmsh, OpenFOAM, CalculiX, and BPM—to support parallel, co-simulated workflows. In a UAV wing optimization case study, the platform autonomously executed over 400 parameter configuration iterations with 100% success rate, zero human intervention, and no mesh failure or solver divergence.
Application Category
📝 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 Optimization Engineers, each powered by LLM with domain-specific knowledge. Agent-agent collaboration is achieved through file-mediated communication for data provenance and reproducibility, while a comprehensive memory system maintains project context, execution history, and retrieval-augmented domain knowledge to ensure reliable decision-making across the workflow. The system integrates FreeCAD, Gmsh, OpenFOAM, CalculiX, and BPM acoustic analysis, enabling parallel multidisciplinary simulations while maintaining computational accuracy. The framework is validated through UAV wing optimization. This work demonstrates that agentic-AI-enabled AI engineers has the potential to perform complex engineering tasks autonomously. Remarkably, the automated workflow achieved a 100% success rate across over 400 parametric configurations, with zero mesh generation failures, solver convergence issues, or manual interventions required, validating that the framework is trustworthy.
Problem
Research questions and friction points this paper is trying to address.
Automating multidisciplinary engineering design collaboration through AI teams
Reducing development time and cost in complex computational design workflows
Enabling autonomous AI engineers to perform trustworthy engineering tasks
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hierarchical multi-agent architecture coordinates specialized AI engineers
File-mediated communication ensures data provenance and reproducibility
Integrated simulation tools enable parallel multidisciplinary design optimization
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