In this episode, Professor Ricardo Vinuesa - Associate Chair for Research and Associate Professor of Aerospace Engineering at the University of Michigan - explores with Neil one of the biggest questions in modern fluid mechanics: can AI help us move beyond faster CFD and toward genuine autonomous scientific discovery? Drawing on his work at the intersection of turbulence, machine learning, explainable AI, reduced-order modeling, and flow control, Neil and Prof. Vineusa discusses the promise and limits of foundation models for fluids, why the right latent representations may matter more than simply scaling data, and how agentic AI systems could uncover physical mechanisms that humans might otherwise miss.Agentic Exploration of PDE Spaces using Latent Foundation Models for Parameterized Simulations — Abhijeet Vishwasrao et al.
https://arxiv.org/abs/2604.09584
The episode’s most direct follow-up: multi-agent LLMs and latent foundation models autonomously explore flow physics in a tandem-cylinder setup.

Enhancing computational fluid dynamics with machine learning — Ricardo Vinuesa, Steven L. Brunton
https://doi.org/10.1038/s43588-022-00264-7
A concise roadmap for useful ML in CFD, from faster simulations and turbulence modelling to reduced-order models.

Identifying regions of importance in wall-bounded turbulence through explainable deep learning — Andrés Cremades et al.
https://doi.org/10.1038/s41467-024-47954-6
Uses explainable AI to identify flow structures that matter for prediction and control, not just visually striking turbulence features.

β-Variational autoencoders and transformers for reduced-order modelling of fluid flows — Alberto Solera-Rico et al.
https://doi.org/10.1038/s41467-024-45578-4
Shows how disentangled latent spaces, autoencoders, and transformers can support interpretable reduced-order models of nonlinear flows.

Improving turbulence control through explainable deep learning — Miguel Beneitez et al.
https://arxiv.org/abs/2504.02354
Links explainable AI with deep reinforcement learning to target turbulence-sustaining mechanisms, with relevance for flow control, drag reduction, and energy efficiency.LinksVinuesaLabhttps://www.vinuesalab.com/Ricardo Vinuesa — University of Michigan Aerospace Engineeringhttps://aero.engin.umich.edu/people/ricardo-vinuesa/AI and ML for Fluid Dynamics course — Ricardo Vinuesa & Sergio Hoyashttps://www.flowthermolab.com/courses/ai-ml-for-fluids/VinuesaLab YouTube channelhttps://www.youtube.com/@VinuesaLabAI for Fluid Mechanics, Sustainability & XAI — Ricardo Vinuesahttps://www.youtube.com/watch?v=TOfwf4ffPnURicardo Vinuesa — Modelling and controlling turbulent flows through deep learninghttps://www.youtube.com/watch?v=0AOY_agZ8WMChapters00:00 Podcast Intro03:20 The Evolution of Foundation Models in Fluid Dynamics10:22 Understanding Explainable AI in Fluid Mechanics15:34 Challenges in Data Fidelity for Foundation Models20:29 Machine Learning vs. Reduced Order Modeling24:22 The Shift in Focus: Turbulence Modeling to Surrogate Models29:48 Exploring Agentic Systems for Scientific Discovery37:21 Exploring Latent Representations in Fluid Dynamics40:40 The Role of AI in Autonomous Discovery41:57 Bridging Fluid Mechanics and Computer Science45:28 Data-Driven vs Physics-Driven Models51:34 The Role of Academia in AI and Fluid Mechanics56:27 Optimization and Control in Machine Learning01:00:28 Future of AI in Fluid Dynamics: Beyond ChatGPT