This perspective paper argues that Explainable AI (XAI) combined with causal reasoning is essential for extracting scientific insights from complex AI models that often outperform human capabilities. It proposes XAI as a unifying framework to foster human-AI collaboration across scientific discovery, engineering optimization, and system certification.

This paper introduces an AI Fluid Scientist framework that automates the entire experimental fluid mechanics workflow, from hypothesis generation to manuscript preparation, using a multi-agent LLM system and a computer-controlled water tunnel. It demonstrates the framework's ability to reproduce benchmarks, discover new phenomena, and generate robust scientific findings with minimal human intervention.