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

DOI10.1007/S10569-022-10101-8zbMath1505.68038OpenAlexW4302027440MaRDI QIDQ2104214

Yanyan Li

Publication date: 9 December 2022

Published in: Celestial Mechanics and Dynamical Astronomy (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10569-022-10101-8

zbMATH Keywords

spherical harmonicsmachine learningastrodynamicsgravity field modelingphysics-informed neural networksmall-bodies

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Dynamics of a system of particles, including celestial mechanics (70F99) Mathematical modeling or simulation for problems pertaining to mechanics of particles and systems (70-10)

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Uses Software

• DiffSharp

Cites Work

• Small body surface gravity fields via spherical harmonic expansions
• Exterior gravitation of a polyhedron derived and compared with harmonic and mascon gravitation representations of asteroid 4769 Castalia
• Mixed-model gravity representations for small celestial bodies using mascons and spherical harmonics
• Physics-informed neural networks for gravity field modeling of the Earth and Moon
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
• Uniform Representation of the Gravitational Potential and its Derivatives
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