Machine Learning for Small Bodies in the Solar System (eBook, ePUB)
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Machine Learning for Small Bodies in the Solar System provides the latest developments and methods in applications of Machine Learning (ML) and Artificial Intelligence (AI) to different aspects of Solar System bodies, including dynamics, physical properties, and detection algorithms. Offering a practical approach, the book encompasses a wide range of topics, providing both readers with essential tools and insights for use in researching asteroids, comets, moons, and Trans-Neptunian objects. The inclusion of codes and links to publicly available repositories further facilitates hands-on learnin...
Machine Learning for Small Bodies in the Solar System provides the latest developments and methods in applications of Machine Learning (ML) and Artificial Intelligence (AI) to different aspects of Solar System bodies, including dynamics, physical properties, and detection algorithms. Offering a practical approach, the book encompasses a wide range of topics, providing both readers with essential tools and insights for use in researching asteroids, comets, moons, and Trans-Neptunian objects. The inclusion of codes and links to publicly available repositories further facilitates hands-on learning, enabling readers to put their newfound knowledge into practice. Machine Learning for Small Bodies in the Solar System serves as an invaluable reference for researchers working in the broad fields of Solar System bodies; both seasoned researchers seeking to enhance their understanding of ML and AI in the context of Solar System exploration or those just stepping into the field looking for direction on methodologies and techniques to apply ML and AI in their work. - Provides a practical reference to applications of machine learning and artificial intelligence to small bodies in the Solar System - Approaches the topic from a multidisciplinary perspective, with chapters on dynamics, physical properties and software development - Includes code and links to publicly available repositories to allow readers practice the methodology covered
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