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Found 4 papers, 1 papers with code
Robust Calibration For Improved Weather Prediction Under Distributional Shift
In this paper, we present results on improving out-of-domain weather prediction and uncertainty estimation as part of the \texttt{Shifts Challenge on Robustness and Uncertainty under Real-World Distributional Shift} challenge.
On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods
Recent developments in Machine Learning approaches for modelling physical systems have begun to mirror the past development of numerical methods in the computational sciences.
Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors
The application is challenging due to the large image resolutions in which defects are very small and hardly captured by the commonly used anchor sizes, and also due to the small size of the available dataset.
An Optimal Control Approach to Learning in SIDARTHE Epidemic model
We formulate the problem in terms of a functional risk that depends on the learning variables through the solutions of a dynamic system.
