a public good project by the
Synthesis
Company
of California

© 2026

Constraining the thermal properties of planetary surfaces using machine learning: Application to airless bodies | doi.page

0 citations0 references

Google Scholar doi.org

Constraining the thermal properties of planetary surfaces using machine learning: Application to airless bodies

Icarus·2019·Vol. 325, pp. 16–30

Citations Over TimeTop 16% of 2019 papers

Saverio Cambioni, Marco Delbó, A. J. Ryan, Roberto Furfaro, Erik Asphaug

Related Papers

→ The regolith properties of the Chang'e-5 landing region and the ground drilling experiments using lunar regolith simulants(2019)63 cited
→ Deducing Lunar Regolith Porosity From Energetic Neutral Atom Emission(2022)24 cited
→ Modeling the Evolution of Lunar Regolith: 1. Formation Mechanism Through Individual Simple Impact Craters(2023)16 cited
Review of Engineering Geological Characteristics of Lunar Regolith(2013)
The stochastic variability of asteroidal regolith depths(1982)