Physics-informed neural networks for gravity field modeling of small bodies-Reference-Cited by-同舟云学术

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

Published:2022-10 Issue:5 Volume:134 Page:
ISSN:0923-2958
Container-title:Celestial Mechanics and Dynamical Astronomy
language:en
Short-container-title:Celest Mech Dyn Astron

Author:

Martin John^ORCID,Schaub Hanspeter^ORCID

Funder

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Space and Planetary Science,Astronomy and Astrophysics,Applied Mathematics,Computational Mathematics,Mathematical Physics,Modeling and Simulation

Link

https://link.springer.com/content/pdf/10.1007/s10569-022-10101-8.pdf

Reference35 articles.

1. Baydin, A.G., Pearlmutter, B.A., Siskind, J.M.: Automatic differentiation in machine learning: a survey. J. Mach. Learn. Res. 18, 1–43 (2018)

2. Chesley, S.R., et al.: Trajectory estimation for particles observed in the vicinity of 101955 Bennu. J. Geophys. Res. Planets (2010). https://doi.org/10.1029/2019JE006363

3. Han, S.-C., Jekeli, C., Shum, C.K.: Efficient gravity field recovery using in situ disturbing potential observables from CHAMP. Geophys. Res. Lett. 29(16), 1–4 (2002). https://doi.org/10.1029/2002gl015180

4. Jayalakshmi, T., Santhakumaran, A.: Statistical normalization and back Propagationfor classification. Int. J. Comput. Theory Eng. 3(1), 89–93 (2011). https://doi.org/10.7763/ijcte.2011.v3.288

5. Jekeli, C.: Potential theory and the static gravity field of the earth. In: Treatise on Geophysics: Second Edition 3, pp. 9–35. issn: 9780444538031. (2015) https://doi.org/10.1016/B978-0-444-53802-4.00056-7.

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Initial orbit determination via artificial intelligence for too-short arcs;Acta Astronautica;2024-09

2. Physics-Informed Neural Networks (PINNs) for gravity field modelling and representation: With application to asteroid EROS;International Workshop on Gravity, Electrical & Magnetic Methods and Their Applications, Shenzhen, China, May 19–22, 2024;2024-08-23

3. Simultaneous navigation and mascon gravity estimation around small bodies;Acta Astronautica;2023-12

4. Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter;Monthly Notices of the Royal Astronomical Society;2023-11-03

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