William Dement confirmed the hypothesis that these periods of brain activation during sleep were correlated with dreaming. In 1953, Eugene Aserinsky and Nathaniel Kleitman noted that the sleep of children began and was periodically interrupted by activation of the electroencephalogram and by bursts of saccadic eye movement, the so-called rapid eye movements, or REMs. Hobson, in International Encyclopedia of the Social & Behavioral Sciences, 2001 2 The Association of Dreaming with REM Sleep ![]() In addition to trying to explain the technical innovations behind successful applications of machine learning in fault physics, we also analyze and discuss shortcomings of machine learning in general and in this specific context. Finally, this work provides an overview of the uses of ML in specific applications such as early warning systems, the study of induced seismicity and earthquake forecasting. We also touch briefly on uses of ML in geodesy. The review subsequently covers applications of ML to geophysical data in solid Earth, with an emphasis on seismology, that has a long history of data-driven approaches. The review covers ML applied to data from laboratory experiments, which provide controlled, relatively noise-free data for the application of ML algorithms. ![]() We first address some basic principles and concepts behind machine learning (ML), and domain relevant considerations for the application of ML to geophysical data. Throughout the review, we explore the use of machine learning for fault physics against the backdrop of the discrepancies between laboratory and field observations. This work reviews recent advances in the application of machine learning in the study of fault rupture, ranging from the laboratory to the Earth. In recent years, the intersection of exponentially increasing amounts of data and cheap computing power, from graphics cards in particular, has led to an increasing adoption of machine learning and deep learning techniques, in geoscience in general, and fault physics in particular. Geophysics has historically been a data-driven field. Bertrand Rouet-Leduc, in Advances in Geophysics, 2020 Abstract ![]() Our goal is not to present a careful and detailed discussion of all the material covered in the chapter, which would consist of not one but several books, but to provide a general overview of the field and the key references necessary to deepen the study of any of its parts. Our presentation will first concentrate on the different approaches to calculate the energy of a system for a certain nuclear configuration, which serve as starting point for the dynamic methodologies that study its time evolution, described in the second moiety of the chapter. In the present chapter, we aim to provide a general description of the methods used to simulate systems of biological interest, which range from relatively small drugs formed by tens of atoms to DNA fragments, proteins, and enzymes formed by millions of atoms. Numerical simulations of structural changes and chemical/biochemical processes in such systems have become accurate enough to complement the experimental studies and to shed light on the laboratory observations. Quite probably, the area where the ensuing changes have been more notorious is physical chemistry and, in particular, the analysis of the energetic and dynamic aspects of the molecular systems. Aldegunde, in Computer Aided Chemical Engineering, 2016 AbstractĬhemistry is not alien to the scientific revolution triggered by the generalization of the usage of computers.
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