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🎙 About The Episode
Dr. Ricardo Vinuesa is an Associate Professor at the Department of Engineering Mechanics, at KTH Royal Institute of Technology in Stockholm. He is also a Researcher at the AI Sustainability Center in Stockholm and Vice Director of the KTH Digitalization Platform.
He received his Ph.D. in Mechanical and Aerospace Engineering from the Illinois Institute of Technology in Chicago. His research combines numerical simulations and data-driven methods to understand and model complex wall-bounded turbulent flows, such as the boundary layers developing around wings, obstacles, or the flow through ducted geometries. He has also led several international initiatives within sustainable and interpretable AI, which have led to highly influential articles in the literature.
Dr. Vinuesa’s research is funded by the Swedish Research Council (VR) and the Swedish e-Science Research Centre (SeRC). He has also received the Göran Gustafsson Award for Young Researchers.
- 🌍 Web of Ricardo's research group: https://www.vinuesalab.com/
- 🌍 His course on machine learning and engineering at KTH (planning on making some material available): FSM3001 Data-driven Methods in Engineering 7.5 credits
- 🌍 High-fidelity simulation of turbulent wing: DNS Re=400000 NACA4412
- 🌍 AI and sustainability: "The role of artificial intelligence in achieving the Sustainable Development Goals"
- 🧠 AI and interpretability: Interpretable deep-learning models to help achieve the Sustainable Development Goals
- ✍️ Articles on temporal predictions in turbulence through deep learning:
- Predictions of turbulent shear flows using deep neural networks
- Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence
- ✍️ Articles on non-intrusive sensing through deep learning:
- Convolutional-network models to predict wall-bounded turbulence from wall quantities
- From coarse wall measurements to turbulent velocity fields through deep learning
- ✍️ Article on RANS modeling through deep learning:
- An interpretable framework of data-driven turbulence modeling using deep neural networks
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