14. Scientific Intelligence AI×Science: AI × HPC Convergence
Chapter focus: AI×Science is the convergence frontier of "AI compute × supercomputing": using AI compute and model capabilities to reshape the accuracy and scale of scientific computing. Half of this chapter covers the engineering route of scientific foundation models and AI4S compute infrastructure; the other half covers disciplinary applications from life, weather, fluid, and materials to molecular dynamics, quantum chemistry, and genomics.
🧭 Reading Map
- Prerequisites: 13. Inference Engines + 14. Industry Apps — AI×Science builds on model engineering and industry practice
- Role: closing chapter of Part 5, L5 — AI for Science: weather/life/materials/CFD frontiers and AI4S cluster engineering
- Order: AI4S cluster & engineering (compute base) → domain cases (by interest: weather/bio/materials/CFD) → science foundation models
- Next: none (book finale) — revisit the AI-HPC architecture overview for the full picture
Articles
Engineering readers start with AI4S compute infrastructure and distributed training (linking Chapter 11), then scientific agents and reproducible workflows; disciplinary readers jump straight to their field articles.
Key Takeaways
The engineering mainline of AI×Science is "data-driven + physically falsifiable": AI approximates and discovers, scientific validation falsifies, and only their combination yields credible scientific conclusions.