Technical Approach
How FEAI combines AI with rigorous engineering simulation
The Challenge
Traditional large language models are trained on text data and lack the mathematical foundations needed for accurate physics simulations. Simply asking ChatGPT to “run an FEA simulation” will not produce reliable results.
Our Solution
FEAI uses a hybrid approach that combines:
- Natural Language Understanding - To interpret user intent and design requirements from conversational input
- Parametric Geometry Generation - AI-assisted creation of metamaterial unit cells and lattice structures
- Rigorous FEA Solvers - Industry-standard finite element methods for actual simulation, not AI approximations
- Physics-Informed Neural Networks - For rapid preliminary screening before full FEA runs
The AI Role
The AI in FEAI does not replace the physics engine—it augments the user experience. AI helps translate design intent into parameters, suggests optimization directions, and interprets results. The actual simulation math remains deterministic and verifiable.
Training Data
Our models are trained on peer-reviewed metamaterial research, validated simulation datasets, and engineering literature. This domain-specific training enables accurate geometry suggestions and meaningful design guidance.
Validation
Every simulation result can be exported and verified in commercial FEA software. We provide mesh files, boundary conditions, and material properties for independent validation.
Key Principle
AI should make engineering more accessible, not replace engineering judgment. FEAI accelerates exploration while maintaining scientific rigor.