Machine learning methods for nonlinear dimensionality reduction of the 
thermospheric density field

Nateghi, Vahid; Manzi, Matteo

doi:10.1016/j.asr.2023.08.010

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Machine learning methods for nonlinear dimensionality reduction of the thermospheric density field

Nateghi, V., & Manzi, M. (2023). Machine learning methods for nonlinear dimensionality reduction of the thermospheric density field. Advances in Space Research, 72(10), 4106-4114. doi:10.1016/j.asr.2023.08.010.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-B971-3 Version Permalink: https://hdl.handle.net/21.11116/0000-000E-3AE5-F

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Creators:
Nateghi, Vahid¹, Author
Manzi, Matteo², Author

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1Data-driven Modeling of Complex Physical Systems, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_3366463
2CrunchDAO, Paris, France, ou_persistent22

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Dates: Date issued: 2023

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Identifiers: DOI: 10.1016/j.asr.2023.08.010
Other: pubdata_escidoc:3530250

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Title: Advances in Space Research

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Pages: - Volume / Issue: 72 (10) Sequence Number: - Start / End Page: 4106 - 4114 Identifier: ISSN: 02731177