AI Orchestration in Engineering Simulation

LLMs like GPT-40 are at the core of this innovation, translating natural language requests into executable simulation commands. Their ability to process and generate human-like text enables a seamless interface between users and complex simulation engines. This section explores how LLMs are integrated to interpret user prompts, map them to predefined function calls, and generate structured outputs like JSON for simulation execution.

Ground AI is a critical enhancement to LLM-driven systems, ensuring factual accuracy and reliability. It involves verification mechanisms that validate outputs against reliable data or predefined schemas. The self-review method, particularly with single-LLM agents, prompts the LLM to review and correct its own outputs, focusing on structural validation (e.g., checking vector lengths against schema). This iterative refinement significantly reduces error rates and improves the consistency of generated scenarios.

This advanced approach deploys multiple LLM agents in parallel to generate simulation scenarios. Each agent works independently, and their outputs are validated sequentially until a valid scenario is produced. Multi-agent systems significantly increase the probability of generating a valid scenario, especially for complex tasks, by combining collective efforts and reducing individual agent failure rates. While computationally more expensive, optimizing the number of agents offers a balance between cost and enhanced reliability.