Machine Learning in Astrophysical and Space Physics Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Physics General".
Deadline for manuscript submissions: 20 February 2026 | Viewed by 11
Special Issue Editors
Interests: mathematical modelling; astrophysics; plasma physics; space physics
Interests: solar physics; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning is a rapidly growing field, yielding breakthroughs in many areas of science, including astrophysics and space physics. By allowing computers to learn from data and make predictions or decisions without being explicitly programmed, machine learning provides powerful tools for solving complex and large-scale scientific problems. In astrophysics and space physics, these tools are increasingly being used to solve a wide variety of problems, such as classifying and analyzing astronomical images, detecting exoplanets, modeling stellar activity, predicting space weather, and improving the accuracy and speed of data processing. Machine learning algorithms are also proving valuable for accelerating numerical modeling and solving inverse problems that are often computationally intensive in traditional approaches. With the increasing availability of large observational and modeling datasets, the integration of machine learning methods opens new opportunities for discovery and innovation in understanding the processes occurring in the Universe. This Special Issue welcomes original research papers that develop, adapt, and apply machine learning methods to problems in astrophysics and space physics. Papers that explore new methodologies, improve predictive performance, or provide new insights into physical phenomena using data-driven approaches are particularly welcome.
Dr. Elena P. Popova
Dr. Marianna Korsós
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- artificial intelligence
- astrophysics
- space physics
- simulations
- computer algorithms
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