Machine Learning (ML) and Artificial Intelligence (AI) are transforming fluid dynamics, enabling faster simulations, smarter turbulence modeling, and innovative flow-control strategies. For CFD engineers and students, upskilling in AI is essential to stay competitive, accelerate research, and contribute to the next generation of engineering solutions.
A Collaboration between the University of Michigan and Flowthermolab
This initiative brings together world-class expertise to advance AI and Machine Learning in Fluid Dynamics through dedicated training and research programs. The collaboration will equip students, researchers, and engineers with cutting-edge skills in CFD, data-driven turbulence modeling, and sustainable engineering applications.
Agenda:
Building from classical to modern machine-learning and deep-learning methods for fluid mechanics, including CFD, turbulence analysis, prediction, optimization, control and reduced-order modeling, etc.
Course Fee:
Fee: $200 (incl. Tax)
More information
Missed the live workshop? You can still enroll now to watch the full recordings, learn from the experts, and receive your certificate of completion.
Main Instructor
Prof. Ricardo Vinuesa
Professor Ricardo Vinuesa is a leading researcher at the intersection of fluid dynamics, artificial intelligence (AI), and sustainability. He is currently an Associate Professor of Aerospace Engineering at the University of Michigan, USA.
He studied Mechanical Engineering at the Polytechnic University of Valencia (UPV) in Spain, followed by an MSc and PhD in Mechanical and Aerospace Engineering at the Illinois Institute of Technology (IIT), Chicago. Before joining Michigan, he was an Associate Professor at KTH Royal Institute of Technology in Stockholm, where he also served as Vice Director of the KTH Digitalization Platform and Lead Faculty at the KTH Climate Action Centre.
Professor Vinuesa’s research combines high-fidelity simulations with machine learning methods, including deep neural networks, reinforcement learning, and explainable AI, to advance turbulence prediction, optimization, and flow control. His influential Nature Communications (2020) paper on AI and the UN Sustainable Development Goals demonstrated both the opportunities and risks of AI for global sustainability.
He has published over 100 papers and received prestigious honors, including a European Research Council (ERC) Consolidator Grant, the Göran Gustafsson Award, and the IIT Outstanding Young Alumnus Award.
Course content in detail
- Theory Topics:
- Point and temporal predictions: multi-layer perceptron and recurrent neural networks.
- Physics-informed neural networks.
- Spatial predictions: computer-vision methods.
- Explainable deep learning and applications to sensing.
- Optimization and control: deep reinforcement learning.
- Reduced-order models: autoencoders, generative AI, and modern architectures.
- Practical Topics:
- Design and training of a multi-layer perceptron and a long short-term network.
- Design and training of autoencoders
- Certificate
Frequently asked question
- Do I get a certificate?
Yes, once you attend the sessions and complete the assessment - What if I don’t understand some portion or need to clarify some doubts?
You can ask all your questions directly to the instructors during the workshop or later through forum discussions - Should I know programming to learn this course?
Basic knowledge of Python will be useful, but not necessary - Is there any prerequisite?
Knowledge of CFD, Fluid dynamics, and coding with Python will be useful
Who is this course for?
- Practicing CFD Engineers who want to upskill and stay relevant in the job market
- Bachelor students (Mechanical, Aerospace, Chemical, Civil Engineering)
- Researchers, master’s students, and PhD students
Other details
- Total access to recordings of live sessions: 1 year
- Computer requirement: Minimum 4 GB RAM and i3 processor
Course Includes
- 13 Lessons
Ratings and Reviews
Ntenis Istrati I liked the theoretical aspect as well as the practical session. My expectations were to introduce me to the methods and learn the state of the art for turbulence modelling today. I really met my expectations, while I could kindly point out that I would personally need a bit more extend explanation for the basic stuff and some important details as I am new to AI/ML (e.g. POD, how Kernel sweeps).
Rajat Very good course to deep dive into ML applications related to Fluid Dynamics and CFD.
Shreyas Bidadi The course covered the fundamentals as well as the current state-of-the-art research in AI for fluid mechanics. The lectures were excellent and the lab material helped me better absorb the content.
One person found this helpful. Giovanni Marinello Fantastic course!
Min Zhang To be honest, I thought this course would just reinforce what I had already learned about ML and maybe help me go a bit deeper through the practical sessions. But Dr. Ricardo Vinuesa completely surprised me—in the best way! His teaching was awesome, and for the first time I feel like I truly understand these concepts. Thank you so much, Dr. Ricardo Vinuesa!
2 people found this helpful. 
Drives the motivation well, the course is crafted such that it concentrates AI/ML's focus on solving engineering problems (although speech and text recognition (embedding )is what most of the materials and documentation advocates out there). So totally a good beginner friendly course for AI/ML in engineering