conference

Characterization Of Neural Ordinary Differential Equations For Astrodynamics Applications

How can we discover dynamics directly from data?

Sarah Wielgosz, John Martin

Characterization Of Neural Ordinary Differential Equations For Astrodynamics Applications

MW-NeRF: Multi-Wavelength Nerf Models For Spacecraft Modeling In Shadowed Environments

Multi-spectral wavelengths help NeRFs see in the dark

Logan Selph, John Martin

MW-NeRF: Multi-Wavelength Nerf Models For Spacecraft Modeling In Shadowed Environments

Risk-Sensitive Reinforcement Learning for Designing Robust Low-Thrust Interplanetary Trajectories

(2025 Breakwell Paper Award) Deep reinforcement learning with adherence to prescribed risk profiles

Aarun Srinivas, John Martin

Risk-Sensitive Reinforcement Learning for Designing Robust Low-Thrust Interplanetary Trajectories

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

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

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