BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Dario Izzo (European Space Agency) DTSTART:20220630T160000Z DTEND:20220630T170000Z DTSTAMP:20260423T003242Z UID:MPML/81 DESCRIPTION:Title: Ge odesy of irregular small bodies via neural density fields: geodesyNets \nby Dario Izzo (European Space Agency) as part of Mathematics\, Physics a nd Machine Learning (IST\, Lisbon)\n\n\nAbstract\nThe problem of determini ng the density distribution of celestial bodies from the induced gravitati onal pull is of great importance in astrophysics as well as space engineer ing (thinking of situations where spacecraft need to perform orbital and s urface proximity operations). Knowledge of a body density distribution pro vides also great insights on the body's origin and composition. In practic e\, the state-of-the-art approaches for modelling the gravity field of ext ended bodies are spherical harmonics models\, mascon models and polyhedral gravity models. All of these\, however\, while being widely studied and d eveloped since the early works from Laplace\, introduce requirements such as knowledge of a shape model\, assumption of a homogeneous internal densi ty\, being outside the\nBrillouin sphere\, etc...\n\n\nIn this talk\, we i ntroduce and explain Neural Density Fields\, a new approach to represent t he density of extended bodies and learn its accurate form inverting data f rom gravitational accelerations\, orbits or the gravity potential. The res ulting deep learning model\, called geodesyNets is able to compete with c lassical approaches while solving most of their limitations. We also intro duce eclipseNets\, a deep learning model based on related ideas and able t o learn the eclipse shadow cones of irregular bodies\, thus allowing highl y precise propagation and stability studies.\n LOCATION:https://researchseminars.org/talk/MPML/81/ END:VEVENT END:VCALENDAR