Artificial Intelligence is gaining popularity in many fluid mechanics related applications. For many fluid mechanical applications it’s common to have huge amounts of data from both experiments or simulations. Using machine learning techniques we can process and extract useful patterns in complex data in order to simplify problems are create reduced order models. This seminar focuses on both recent advances and new applications.
Programme
| Time | ||
|---|---|---|
| 09:30 | Registration | |
| 10:00 | Welcome | Anders Christian Olesen, Vestas Wind Systems A/S, Denmark |
| 10:15 | Data-driven Fluid Mechanics and Machine Learning | Mahdi Abkar, Department of Mechanical and Production Engineering, Section of Fluid & Energy, Aarhus University |
| 11:15 | Coffee break | |
| 11:30 | 3D deep-learning for faster product development and CFD frontloading | Pierre Baque, CEO, Neural concept |
| 12:00 | Lunch break and tour | |
| 13:15 | Modeling and controlling turbulent flows through deep learning | Ricardo Vinuesa, KTH Royal Institute of Technology |
| 14:00 | DANSIS Graduate Award 2022 Ceremony and Presentation | |
| 14:30 | Coffee break | |
| 15:00 | How can AI support the CFD engineer? | Mark Farrall, Siemens Digital Industries Software |
| 15:30 | Deep Reinforcement Learning applied to simulated wind flow data | Ewan Machefaux, Vestas Wind Systems A/S |
| 16:00 | Closing remarks | Knud Erik Meyer, DANSIS chairman |
