Celestial Mechanics and Dynamical Astronomy - Call for papers: “Machine Learning in Celestial Mechanics and Dynamical Astronomy”

The journal Celestial Mechanics and Dynamical Astronomy is planning to publish a thematic collection of papers on "Machine Learning in Celestial Mechanics and Dynamical Astronomy" (this opens in a new tab), which hopes to include original articles from leading researchers in the field.

In the last two decades, machine learning has found application in a wide range of areas of science and scientific computing. In a number of cases this has led to new developments beyond the simple application of existing machine learning algorithms. More recently, machine learning has found interesting applications in the fields of astronomy, astrophysics and in space engineering, where machine learning for optimal control and trajectory design was applied to solve problems in mission analysis, space traffic management and on board autonomy.

For this topical collection, we invite authors to submit high-quality original contributions that address the many aspects of the use and development of machine learning in celestial mechanics and dynamical astronomy.

Authors are encouraged to submit papers on one or more of the following topics:

• Coordinate transformation
• Discovery of dynamical laws
• Motion classification
• Modelling of physical properties from data
• Uncertainty quantification
• Trajectory design
• Orbit determination (ground-based and space-based)
• Attitude determination

Deadline

The ML Topical Collection is open for submissions since 01 September 2022. All papers will be reviewed as in normal issues. Of course, if a paper is not included in the Topical Collection, it can be published in a regular issue.

Editors

Massimiliano Vasile (Associate Editor),
Xiyun Hou (Associate Editor),
Roberto Furfaro (Guest editor) and
Alessandra Celletti (Editor-in-Chief)