Download or read book Deep Learning in Solar Astronomy written by Long Xu and published by Springer Nature. This book was released on 2022-05-27 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition. Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices. This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them.
Download or read book Machine Learning in Heliophysics written by Thomas Berger and published by Frontiers Media SA. This book was released on 2021-11-24 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Learning Techniques for Space Weather written by Enrico Camporeale and published by Elsevier. This book was released on 2018-05-31 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields. - Collects many representative non-traditional approaches to space weather into a single volume - Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists - Includes free software in the form of simple MATLAB® scripts that allow for replication of results in the book, also familiarizing readers with algorithms
Download or read book 2018 International Interdisciplinary PhD Workshop IIPhDW written by IEEE Staff and published by . This book was released on 2018-05-09 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The International Interdisciplinary PhD Workshop will take place in winouj cie between 9 May and 12 May 2018 The goal is to gather PhD students in order to share knowledge and discuss problems related to their research and scientific interests The Workshop enables the participants to gain valuable experience that will reflect in their professional research careers Importantly, the event also provides the opportunity to integrate with the scientific community and develop informal contacts The session chairs are among the most renowned experts in the fields covered by the Workshop Thus, attending the event is the only way to meet these specialists and possibly ask some intricate questions
Download or read book Machine Learning with Neural Networks written by Bernhard Mehlig and published by Cambridge University Press. This book was released on 2021-10-28 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.
Download or read book The Solar Dynamics Observatory written by Phillip Chamberlin and published by Springer Science & Business Media. This book was released on 2012-05-05 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is dedicated to the Solar Dynamics Observatory (SDO), which was launched 11 February 2010. The articles focus on the spacecraft and its instruments: the Atmospheric Imaging Assembly (AIA), the Extreme Ultraviolet Variability Experiment (EVE), and the Helioseismic and Magnetic Imager (HMI). Articles within also describe calibration results and data processing pipelines that are critical to understanding the data and products, concluding with a description of the successful Education and Public Outreach activities. This book is geared towards anyone interested in using the unprecedented data from SDO, whether for fundamental heliophysics research, space weather modeling and forecasting, or educational purposes. Previously published in Solar Physics journal, Vol. 275/1-2, 2012. Selected articles in this book are published open access under a CC BY-NC 2.5 license at link.springer.com. For further details, please see the license information in the chapters.
Download or read book The Principles of Deep Learning Theory written by Daniel A. Roberts and published by Cambridge University Press. This book was released on 2022-05-26 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Download or read book Universal written by Brian Cox and published by Da Capo Press. This book was released on 2017-03-28 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: An awe-inspiring, unforgettable journey of scientific exploration from Brian Cox and Jeff Forshaw, the international bestselling authors of Why Does E=MC2? and The Quantum Universe, with 55 black-&-white and 45 full-color pages featuring photographs, diagrams, maps, tables, and graphs. We dare to imagine a time before the Big Bang, when the entire universe was compressed into a space smaller than an atom. And now, as Brian Cox and Jeff Forshaw show, we can do more than imagine: we can understand. Universal takes us on an epic journey of scientific exploration. It reveals how we can all come to grips with some of the most fundamental questions about our Earth, Sun, and solar system--and the star-filled galaxies beyond. How big is our solar system? How quickly is space expanding? How big is the universe? What is it made of? Some of these questions can be answered on the basis of observations you can make in your own backyard. Other answers draw on the astonishing information now being gathered by teams of astronomers operating at the frontiers of the known universe. At the heart of all this lies the scientific method. Science reveals a deeper beauty and connects us to each other, to our world, and to our universe. Science reaches out into the unknown. As Universal demonstrates, if we dare to imagine, we can do the same.
Download or read book Machine Learning for Small Bodies in the Solar System written by Valerio Carruba and published by Elsevier. This book was released on 2024-10-29 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Knowledge Discovery in Big Data from Astronomy and Earth Observation written by Petr Skoda and published by Elsevier. This book was released on 2020-04-10 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. - Addresses both astronomy and geosciences in parallel, from a big data perspective - Includes introductory information, key principles, applications and the latest techniques - Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields
Download or read book Machine Learning for Physics and Astronomy written by Viviana Acquaviva and published by Princeton University Press. This book was released on 2023-08-15 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on introduction to machine learning and its applications to the physical sciences As the size and complexity of data continue to grow exponentially across the physical sciences, machine learning is helping scientists to sift through and analyze this information while driving breathtaking advances in quantum physics, astronomy, cosmology, and beyond. This incisive textbook covers the basics of building, diagnosing, optimizing, and deploying machine learning methods to solve research problems in physics and astronomy, with an emphasis on critical thinking and the scientific method. Using a hands-on approach to learning, Machine Learning for Physics and Astronomy draws on real-world, publicly available data as well as examples taken directly from the frontiers of research, from identifying galaxy morphology from images to identifying the signature of standard model particles in simulations at the Large Hadron Collider. Introduces readers to best practices in data-driven problem-solving, from preliminary data exploration and cleaning to selecting the best method for a given task Each chapter is accompanied by Jupyter Notebook worksheets in Python that enable students to explore key concepts Includes a wealth of review questions and quizzes Ideal for advanced undergraduate and early graduate students in STEM disciplines such as physics, computer science, engineering, and applied mathematics Accessible to self-learners with a basic knowledge of linear algebra and calculus Slides and assessment questions (available only to instructors)
Download or read book Artificial Neural Networks written by P.J. Braspenning and published by Springer Science & Business Media. This book was released on 1995-06-02 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents carefully revised versions of tutorial lectures given during a School on Artificial Neural Networks for the industrial world held at the University of Limburg in Maastricht, Belgium. The major ANN architectures are discussed to show their powerful possibilities for empirical data analysis, particularly in situations where other methods seem to fail. Theoretical insight is offered by examining the underlying mathematical principles in a detailed, yet clear and illuminating way. Practical experience is provided by discussing several real-world applications in such areas as control, optimization, pattern recognition, software engineering, robotics, operations research, and CAM.
Download or read book Observing the Sun written by Jamey L. Jenkins and published by Springer Science & Business Media. This book was released on 2013-07-19 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Observing the Sun” is for amateur astronomers at all three levels: beginning, intermediate, and advanced. The beginning observer is often trying to find a niche or define a specific interest in his hobby, and the content of this book will spark that interest in solar observing because of the focus on the dynamics of the Sun. Intermediate and advanced observers will find the book invaluable in identifying features (through photos, charts, diagrams) in a logical, orderly fashion and then guiding the observer to interpret the observations. Because the Sun is a dynamic celestial body in constant flux, astronomers rarely know for certain what awaits them at the eyepiece. All features of the Sun are transient and sometimes rather fleeting. Given the number of features and the complex life cycles of some, it can be a challenging hobby. “Observing the Sun” provides essential illustrations, charts, and diagrams that depict the forms and life cycles of the numerous features visible on the Sun.
Download or read book Solar Energy Forecasting and Resource Assessment written by Jan Kleissl and published by Academic Press. This book was released on 2013-06-25 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solar Energy Forecasting and Resource Assessment is a vital text for solar energy professionals, addressing a critical gap in the core literature of the field. As major barriers to solar energy implementation, such as materials cost and low conversion efficiency, continue to fall, issues of intermittency and reliability have come to the fore. Scrutiny from solar project developers and their financiers on the accuracy of long-term resource projections and grid operators' concerns about variable short-term power generation have made the field of solar forecasting and resource assessment pivotally important. This volume provides an authoritative voice on the topic, incorporating contributions from an internationally recognized group of top authors from both industry and academia, focused on providing information from underlying scientific fundamentals to practical applications and emphasizing the latest technological developments driving this discipline forward. - The only reference dedicated to forecasting and assessing solar resources enables a complete understanding of the state of the art from the world's most renowned experts. - Demonstrates how to derive reliable data on solar resource availability and variability at specific locations to support accurate prediction of solar plant performance and attendant financial analysis. - Provides cutting-edge information on recent advances in solar forecasting through monitoring, satellite and ground remote sensing, and numerical weather prediction.
Download or read book Bayesian Models for Astrophysical Data written by Joseph M. Hilbe and published by Cambridge University Press. This book was released on 2017-04-27 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.
Download or read book Reviews in astronomy and space sciences written by Christopher H. K. Chen and published by Frontiers Media SA. This book was released on 2024-05-24 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advances in Machine Learning and Data Mining for Astronomy written by Michael J. Way and published by CRC Press. This book was released on 2012-03-29 with total page 746 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.
