Physics-Informed Neural Network Gravity Models
Building high-fidelity gravity models for spaceflight, planetary science, and geodesy using physics-informed neural networks.
In-Situ Model Reconstruction from Spacecraft Measurements
How can we learn the dynamics of novel environments with limited data?
Satellite Fault Detection and Isolation using Deep Learning
How can we reduce the burden on satellite operators to detect and diagnose faults?
Shape Model Acquisition during Spacecraft Rendezvous using Neural Radiance Fields
Use sparse images taken from safe surveillance orbits, construct a 3D shape model of a target spacecraft or debris.
Orbital Mechanics Coordinate Discovery with Deep Learning
How can we construct more efficient coordinates and basis functions to represent complex dynamical systems?
Autonomous Spacecraft Decision Making with Reinforcement Learning
Explore how to safely navigate exotic environments in deep space with autonomous decision making agents with reinforcement learning.