Deep learning in astronomy: a tutorial perspective

Meher, Saroj K.; Panda, Geetanjali

doi:10.1140/epjs/s11734-021-00207-9

Cited by 15 publications

(13 citation statements)

References 118 publications

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“…Traditional tools are incapable of handling such vast amounts of data. Consequently, astronomical research is shifting from a hypothesis-driven to a data-driven methodology (Meher and Panda, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Astronomy is a discipline of science that encompasses the research and examination of all extraterrestrial objects and events. It covers the extremely difficult and complex process of investigating and evaluating astrophysical phenomena and integrates the facets of mathematics, physics, and chemistry to understand the origin, evolution, and functions of the Universe and celestial bodies (Meher and Panda, 2021).…”

Section: Ai In Astronomymentioning

confidence: 99%

“…Theoretical astronomers work to refine existing models and look into their evolution. Conversely, observational astronomers gather information about astronomical objects and locations and conduct in-depth research on stars, planets, galaxies, and other celestial bodies (Meher and Panda, 2021).…”

Section: Ai In Astronomymentioning

confidence: 99%

“…However, the goal of an observational astronomers is to observe, document, and gather information about the subject world. Observational astronomy in the present era has become almost completely virtual (Meher and Panda, 2021).…”

Section: Ai In Astronomymentioning

confidence: 99%

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How Does Artificial Intelligence Help Astronomy? A Review

Patel¹

2022

cse

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Artificial intelligence (AI) is a discipline of computing that focuses mostly on transferring human intelligence and mental processes into machines that can assist humans in many ways. Machine learning (ML) is the approach of choice in AI for creating useful software for computer vision, speech recognition, natural language processing, robot control, and other applications. Some of the most common analyses of large, complicated and multidimensional data sets in astronomy can be performed by using ML methods. It can be used for automating observatory scheduling to increase the effective utilization and scientific return from telescopes. It is also used for image recognition, classification of galaxies and planet recognition. This paper offers an in-depth review of the evolution of artificial intelligence and the use of AI and ML in the field of astronomy, especially for data analysis, image recognition, astronomical scheduling, classification of galaxies and planet recognition. It adds to the existing literature on use of artificial intelligence for astronomical applications and is a useful resource for students and researchers.

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Section: Discussionmentioning

confidence: 99%

Section: Ai In Astronomymentioning

confidence: 99%

Section: Ai In Astronomymentioning

confidence: 99%

Section: Ai In Astronomymentioning

confidence: 99%

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How Does Artificial Intelligence Help Astronomy? A Review

Patel¹

2022

cse

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“…The advancement of information technology has brought the era of new predictive modeling methodology, which is known as machine learning. The application of machine learning is enormously broad from agriculture to astronomic physics (Meher and Panda 2021;Meshram et al 2021), and a recent stateof-the-art review articulates how this emerging technology is used for structural engineering (Thai 2022). Machine learning is classified into supervised, semi-supervised, and unsupervised groups, depending upon the type of supplied data for input and output (Wu and Snaiki 2022).…”

mentioning

confidence: 99%

Machine learning for prediction of wind effects on behavior of a historic truss bridge

Wang¹,

Kim

Kimes³

2022

ABEN

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This paper presents the behavior of a 102-year-old truss bridge under wind loading. To examine the wind-related responses of the historical bridge, state-of-the-art and traditional modeling methodologies are employed: a machine learning approach called random forest and three-dimensional finite element analysis. Upon training and validating these modeling methods using experimental data collected from the field, member-level forces and stresses are predicted in tandem with wind speeds inferred by Weibull distributions. The intensities of the in-situ wind are dominated by the location of sampling, and the degree of partial fixities at the supports of the truss system is found to be insignificant. Compared with quadrantal pressure distributions, uniform pressure distributions better represent the characteristics of wind-induced loadings. The magnitude of stress in the truss members is enveloped by the stress range in line with the occurrence probabilities of the characterized wind speed between 40% and 60%. The uneven wind distributions cause asymmetric displacements at the supports.

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