The workshop invites abstract submissions on any aspect of Machine Learning applied to Fluid Dynamics problems. These include, but are not limited to:
- Data-driven/data-augmented models (e.g., rheology, turbulence modeling, combustion, multiphase, ...);
- ML-assisted reduced-order modelling or surrogate modeling of flows, feature detection, signal processing;
- ML-based flow control or optimization;
- Super-resolution reconstruction of flow fields;
- Uncertainty quantification;
- ML-accelerated flow solvers.
Keynote speakers
Andrea Beck (Stuttgart U.), Adrian Lozano Duran (CalTech), Ching-Yao Lai (Stanford U.), Daniel Worrall (Google Deepmind)
Local organizing committee
Benjamin Sanderse (CWI), Wouter Edeling (CWI), Richard Dwight (TU Delft), Anh Khoa Doan (TU Delft), Bernat Font (TU Delft)
