Architecting Agentic AI Reasoning for Fluid dynamics

This talk explores how modern AI systems can augument fluid dynamics research, moving beyond pattern recognition to engage in deliberative thinking. Drawing on recent advances in reasoning and active inference, these systems can engage in iterative hypothesis, experimentation and verification that scientists employ. Unlike general-purpose LLMs, these domain-adapted models, specifically trained on fluid dynamics literature and problem-solving patterns, can naturally reason while maintaining a persistent knowledge graph where each research session adds new nodes (discovered patterns, mechanistic insights, boundary conditions where theories fail) and strengthens causal edges. Crucially, these systems should make their reasoning transparent and challengeable, enabling a true dialogue where human physical intuition guides AI's systematic exploration of vast parameter spaces. The goal is to position fluid dynamicists not as passive consumers of foundation models, but as architects of transparent AI reasoning systems that extend physical intuition and accelerate progress on longstanding flow problems.