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

Abstract

Scientific machine learning and the advent of the Physics-Informed Neural Network (PINN) show considerable potential in their capacity to identify solutions to complex differential equations. Over the past two years, much work has gone into the development of PINNs capable of solving the gravity field modeling problem -i.e. learning a differentiable form of the gravitational potential from position and acceleration estimates. While the past PINN gravity models (PINN-GMs) have demonstrated advantages in model compactness, robustness to noise, and sample efficiency; there remain key modeling challenges which this paper aims to address. Specifically, this paper introduces the third generation of the Physics-Informed Neural Network Gravity Model (PINN-GM-III) which solves the problems of extrapolation error, bias towards low-altitude samples, numerical instability at high-altitudes, and compliant boundary conditions through numerous modifications to the model's design. The PINN-GM-III is tested by modeling a known heterogeneous density asteroid, and its performance is evaluated using seven core metrics which showcases its strengths against its predecessors and other analytic and numerical gravity models.

Efficient Polyhedral Gravity Modeling in Modern C++ and Python

“…This circumstance and the fact that polyhedral models are often used in studying gravitational fields, e.g., for Eros (Zhang et al, 2010), or as a reference for creating new neural models (Martin & Schaub, 2023) make an easy-to-install implementation necessary.…”