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Carlo Saccardi
PhD
Delft University of Technology (TU Delft)
Physics informed machine learning for atmospheric modeling
The project's aim is to develop novel physics-informed deep learning methods for super-resolution and forecasting of atmospheric data, with a particular focus on atmospheric turbulence. Our approach leverages high-resolution in-situ observations, which provide data that is closer to the ground truth. However, due to the limited availability of these in-situ observations, we integrate them with larger but lower-resolution datasets like CERRA and ERA5 to enhance the resolution of our models. This will enable us to create high-fidelity atmospheric models that are essential for accurately predicting optical turbulence.
Track:
Academic Track
Primary Advisor
Exchange Advisor
Research
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