Machine learning techniques in studies of the interior structure of rocky exoplanets
A&A, 650 (2021) A177
Published online: 2021-06-24
Articles citing this article
The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
Cited article:
This article has been cited by the following article(s):
Fundamentals of Interior Modelling and Challenges in the Interpretation of Observed Rocky Exoplanets
Philipp Baumeister, Francesca Miozzi, Claire Marie Guimond, Marie-Luise Steinmeyer, Caroline Dorn, Shun-Ichiro Karato, Émeline Bolmont, Alexandre Revol, Alexander Thamm and Lena NoackSpace Science Reviews 221 (8) (2025)
https://doi.org/10.1007/s11214-025-01248-5
87 (2025)
https://doi.org/10.1002/9781394355518.ch3
Refractory element abundances in rocky planets and their host stars: Revisiting the compositional link
Xiangxing Chen and Dongdong NiAstronomy & Astrophysics 704 A108 (2025)
https://doi.org/10.1051/0004-6361/202556534
BICEPS: An improved characterization model for low- and intermediate-mass exoplanets
Jonas Haldemann, Caroline Dorn, Julia Venturini, Yann Alibert and Willy BenzAstronomy & Astrophysics 681 A96 (2024)
https://doi.org/10.1051/0004-6361/202346965
Comparison of Machine-learning and Bayesian Inferences for the Interior of Rocky Exoplanets with Large Compositional Diversity
Yong Zhao, Zibo Liu, Dongdong Ni and Zhiyuan ChenThe Astrophysical Journal Supplement Series 272 (2) 35 (2024)
https://doi.org/10.3847/1538-4365/ad3f1c
Machine-learning Inferences of the Interior Structure of Rocky Exoplanets from Bulk Observational Constraints
Yong Zhao, Dongdong Ni and Zibo LiuThe Astrophysical Journal Supplement Series 269 (1) 1 (2023)
https://doi.org/10.3847/1538-4365/acf31a
ExoMDN: Rapid characterization of exoplanet interior structures with mixture density networks
Philipp Baumeister and Nicola TosiAstronomy & Astrophysics 676 A106 (2023)
https://doi.org/10.1051/0004-6361/202346216
Constraining the thermal inertia of Mars utilizing machine learning techniques
Hongqing Song, Jie Zhang, Shuyi Du, et al.Monthly Notices of the Royal Astronomical Society 522 (2) 1697 (2023)
https://doi.org/10.1093/mnras/stad1093
Understanding the interior structure of gaseous giant exoplanets with machine learning techniques
Yong Zhao and Dongdong NiAstronomy & Astrophysics 658 A201 (2022)
https://doi.org/10.1051/0004-6361/202142874