Incorporating Physical Knowledge Into Machine Learning for Planetary Space Physics.

ABSTRACT: Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. In comparison, the previous mission to Saturn, Voyager over 20 years earlier, had onboard a ~70 kB 8-track storage ability. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific q

SUBMITTER: Azari AR 

PROVIDER: S-EPMC9354472 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature