OASiS: Open-source framework for multi-physics simulations with AI agents

OASiS: Open-source Framework for Multi-Physics Simulations with AI Agents

OASiS is an innovative open-source framework designed to bridge the gap between artificial intelligence agents and established finite element solvers. By leveraging the Message Passing Interface (MPI) and the Multi-Communication Protocol (MCP), OASiS enables AI agents to interact with and control multiple physics simulation engines. This integration facilitates complex, multi-physics simulations driven by AI, opening new avenues for research and development in scientific computing and engineering.

What OASiS Does

OASiS acts as an intermediary, allowing AI agents to send commands and parameters to eight different finite element solvers. These solvers are integrated through the MCP protocol, ensuring standardized communication. The framework handles the translation of AI agent outputs into solver-compatible inputs and processes the simulation results for the AI agent to interpret. This bidirectional communication allows for iterative simulation refinement and optimization guided by AI.

Key Features

• Open-source Framework: Accessible and modifiable for community contributions and custom integrations.
• Multi-Physics Simulation Support: Connects to 8 distinct finite element solvers, enabling complex simulations.
• AI Agent Integration: Designed for seamless interaction with AI agents for control and analysis.
• MCP Protocol: Utilizes the Multi-Communication Protocol for standardized and verified communication between components.
• GitHub Repository: Hosted on GitHub for easy access to source code, documentation, and community support.

Who OASiS is For

OASiS is a valuable tool for AI researchers exploring novel applications in scientific computing, developers building intelligent systems for engineering design and analysis, and computational scientists seeking to enhance their simulation workflows with AI-driven optimization and control. It is particularly relevant for those working with complex multi-physics problems where traditional simulation methods can be augmented by the adaptive and learning capabilities of AI agents.