Accelerating Design of ML and AI Experiments in Scientific Simulation

Artificial intelligence (AI) and machine learning (ML) have demonstrated incredible success across industry and scientific fields. This impact is felt across scientific simulation domains, where AI and ML techniques are being used to explore complex patterns, improve accuracy of physics solvers, and accelerate time to insight. However, “black box” integration of AI and ML tools into simulations has yet to demonstrate significant impact, leading practitioners to develop their own implementations and integrations to satisfy their workflow needs. In this proposal, we seek to reduce the barriers to adoption of AI and ML tools for mature scientific simulation codes. In Phase I, we will focus on demonstrating an in situ ML toolbox for coupling mature simulation codes to AI and ML tools. We will leverage our team’s expertise, applying our toolbox to two approaches to computational fluid dynamics relevant to NASA missions, large eddy simulation and Reynolds averaged Navier Stokes, which each have unique requirements and methodologies for incorporating AI and ML for improved model accuracy. By exploring these two regimes of CFD, we seek to highlight the flexibility of our approach to empowering mature simulation codes with cutting edge AI and ML tools, aiming toward other physics domains of interest to NASA in future work. Phase I funding will support this research and development effort, laying the groundwork for improved access to new AI and ML tools, and improved infrastructure for simulation users to experiment with these cutting edge tools.