Physics-Informed Neural Networks (PINNs) for gravity field modelling and representation: With application to asteroid EROS
Author:
Affiliation:
1. College of Science, Zhejiang University of Technology
2. School of Earth and Environment, University of Leeds
3. College of Earth Sciences, Guilin University of Technology
4. Chinese Academy of Geological Sciences
Publisher
Society of Exploration Geophysicists and Chinese Geophysical Society
Link
https://library.seg.org/doi/pdf/10.1190/GEM2024-076.1
Reference14 articles.
1. A review of different mascon approaches for regional gravity field modelling since 1968
2. Gravitational field modelling near irregularly shaped bodies using spherical harmonics: a case study for the asteroid (101955) Bennu
3. Convergence and divergence in spherical harmonic series of the gravitational field generated by high-resolution planetary topography-A case study for the Moon
4. A numerical comparison of spherical, spheroidal and ellipsoidal harmonic gravitational field models for small non-spherical bodies: examples for the Martian moons
5. Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations
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