The Physics-Informed Neural Network Gravity Model: Generation III

TL;DR

This paper introduces the third generation of Physics-Informed Neural Network Gravity Models (PINN-GM-III), which addresses previous limitations like divergence and instability, demonstrating improved robustness and accuracy in modeling gravitational fields, including on an asteroid.

Contribution

The paper presents the PINN-GM-III with design enhancements that mitigate divergence, bias, and instability issues in previous models, advancing the state-of-the-art in physics-informed gravity modeling.

Findings

PINN-GM-III shows improved robustness to divergence and noise.

The model achieves higher accuracy on asteroid gravity data.

Evaluation metrics effectively expose model limitations.

Abstract

Scientific machine learning and the advent of the Physics-Informed Neural Network (PINN) have shown high potential in their ability to solve complex differential equations. One example is the use of PINNs to solve the gravity field modeling problem -- learning convenient representations of the gravitational potential from position and acceleration data. These PINN gravity models, or PINN-GMs, have demonstrated advantages in model compactness, robustness to noise, and sample efficiency when compared to popular alternatives; however, further investigation has revealed various failure modes for these and other machine learning gravity models which this manuscript aims to address. Specifically, this paper introduces the third generation Physics-Informed Neural Network Gravity Model (PINN-GM-III) which includes design changes that solve the problems of feature divergence, bias towards low-altitude samples, numerical instability, and extrapolation error. Six evaluation metrics are proposed to expose these past pitfalls and illustrate the PINN-GM-III's robustness to them. This study concludes by evaluating the PINN-GM-III modeling accuracy on a heterogeneous density asteroid, and comparing its performance to other analytic and machine learning gravity models.