Publications

Reinforcement Learning for Spacecraft Navigation & Environment Characterization in the Planar-Restricted Two-Body Problem

Spacecraft tasking and actuation with DRL

Kenny Getzandanner, John Martin

Reinforcement Learning for Spacecraft Navigation & Environment Characterization in the Planar-Restricted Two-Body Problem

Investigating the Fusion of Mascon and Neural Networks Gravity Models

Fusing mascon and PINN gravity models

Investigating the Fusion of Mascon and Neural Networks Gravity Models

Physics-Informed Neural Networks Gravity Model: Generation III

PINN Gravity Model Generation III

John Martin, Hanspeter Schaub

Physics-Informed Neural Networks Gravity Model: Generation III

Periodic orbit discovery enhanced by physics-informed neural networks

How to discover periodic orbits in arbitrary coordinate descriptions.

John Martin, Hanspeter Schaub

Periodic orbit discovery enhanced by physics-informed neural networks

Analysis of Small-Body Gravity Field Estimation using Physics-Informed Neural Networks and Kalman Filters

How to learn the gravity field online using orbit determination.

John Martin, Hanspeter Schaub

Analysis of Small-Body Gravity Field Estimation using Physics-Informed Neural Networks and Kalman Filters

Physics-Informed Neural Networks for Gravity Field Modeling

John Martin Dissertation

Physics-informed neural networks for gravity field modeling of small bodies

PINN Gravity Model Generation II

John Martin, Hanspeter Schaub

Physics-informed neural networks for gravity field modeling of small bodies

Reinforcement Learning and Orbit-Discovery Enhanced by Small-Body Physics-Informed Neural Network Gravity Models

Reinforcement learning for small-body navigation

John Martin, Hanspeter Schaub

Reinforcement Learning and Orbit-Discovery Enhanced by Small-Body Physics-Informed Neural Network Gravity Models

Physics-informed neural networks for gravity field modeling of the Earth and Moon

PINN Gravity Model Generation I

John Martin, Hanspeter Schaub

Physics-informed neural networks for gravity field modeling of the Earth and Moon