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As a service to the scientific machine learning research community, the SciML organization routinely runs and hosts challenge problems with research datasets in order to facilitate the advancement of scientific machine learning as a discipline. These problems come with open datasets, description of the classical numerical techniques used on the problem, and with starter code utilizing SciML software to help methodological research jump directly to the cutting edge of the field on a practical problem.
Harnessing the magnetic field of the earth for navigation has shown promise as a viable alternative to other navigation systems. A magnetic navigation system collects its own magnetic field data using a magnetometer and uses magnetic anomaly maps to determine the current location. The greatest challenge with magnetic navigation arises when the magnetic field data from the magnetometer on the navigation system encompass the magnetic field from not just the earth, but also from the vehicle on which it is mounted. It is difficult to separate the earth magnetic anomaly field magnitude, which is crucial for navigation, from the total magnetic field magnitude reading from the sensor. The purpose of this challenge problem is to decouple the earth and aircraft magnetic signals in order to derive a clean signal from which to perform magnetic navigation. Baseline testing on the dataset shows that the earth magnetic field can be extracted from the total magnetic field using machine learning (ML). The challenge is to remove the aircraft magnetic field from the total magnetic field using a trained neural network. These challenges offer an opportunity to construct an effective neural network for removing the aircraft magnetic field from the dataset, using an ML algorithm integrated with physics of magnetic navigation.
The Helicopter SciML challenge problem was contributed by the University of South-Eastern Norway. The dataset is derived from a laboratory helicopter from which measurements of the pitch and yaw angles are coupled with measurements of the electrical inputs into the rotaries. Simple first principles derivations for the helicopter physics are given and are demonstrated to not explain the full dynamics of the system. The goal is to learn the missing physics required to give a description of the accurately predicting system.