Handbook On Big Data And Machine Learning In The Physical Sciences In 2 Volumes

This compendium provides a comprehensive collection of the emergent applications of big data, machine learning, and artificial intelligence technologies to present day physical sciences ranging from materials theory and imaging to predictive synthesis and automated research. This area of research is among the most rapidly developing in the last several years in areas spanning materials science, chemistry, and condensed matter physics.Written by world renowned researchers, the compilation of two authoritative volumes provides a distinct summary of the modern advances in instrument — driven data generation and analytics, establishing the links between the big data and predictive theories, and outlining the emerging field of data and physics-driven predictive and autonomous systems.

Vol. 1. Big data methods in experimental materials discovery -- vol. 2. Advanced analysis solutions for leading experimental techniques.

The three-volume handbook showcases the state of the art in the use of concentrated sunlight to produce electricity, industrial process heat, renewable fuels, including hydrogen and low-carbon synthesis gas, and valuable chemical commodities. The handbook illustrates the value and diversity of applications for concentrating solar power to contribute to the expanding decarbonization of multiple cross-cutting energy sectors.Volume 1: Concentrating Solar Thermal Power, provides an overview of key technologies, principles, and challenges of concentrating solar power (CSP) as well as the use of concentrating solar thermal for process heating and district markets. The ten chapters of this volume provide the reader with the technical background on the solar resource for concentrating solar thermal, the principles and design of concentrating optics, and descriptions of state-of-the-art and emerging solar collector and receiver technologies, thermal storage and thermal-to-electric conversion and power cycles for CSP. It also contains a comprehensive summary of operations and maintenance requirements for CSP plants, and commercial CSP plants and markets around the world.Volume 2, Solar Thermochemical Processes and Products, covers the use of concentrated solar radiation as the heat source to drive endothermic chemical reactions to produce renewable fuels and valuable chemical commodities, equivalently storing solar energy in chemical bonds. The thermodynamic underpinnings of a number of approaches to produce fuel and results of demonstrations of solar thermochemical reactors for these processes at prototype scale are presented. Processes presented include thermochemical metal oxide reduction/oxidation cycles to split water and carbon dioxide solar chemical looping reformation of methane to produce synthesis gas, high temperature electrochemistry, and gasification of biomass. Research on the thermochemical storage for CSP and high temperature production of cement and ammonia to illustrate the use concentrated solar energy to produce valuable chemical products are also included.Volume 3 contains reprinted archival papers to support and supplement the material in Volumes 1 and 2. These papers provide background information on the economics and alternative use cases of CSP not covered in Volume 1, and expand on the material related to the chapter topics presented in Volume 2. Potential commercialization, such as prototype and demonstration projects, are highlighted. The papers are intended as a starting point for a more in-depth study of the topics.

In this book Professor Lubin lays out the fundamental physics and mathematics required to radically alter our capabilities in propulsion to enable extreme high-speed space flight both in our solar system and beyond. The case is made that the only currently viable solution to enable this transformation, including relativistic flight for the first interstellar robotic missions, is using large-scale directed energy. Traditional methods of propulsion are not capable of achieving the speed required for these missions, including fast crewed missions to Mars as well as the many robotic missions desired both in our solar system and to the nearest stars. Humanity has now reached a technological tipping point with the ability to project power over vast distances with transformational implications in a wide variety of areas, from propulsion to beaming power throughout our solar system to planetary defence. In a series of over 60 technical papers, the fundamentals of this transformation are outlined and synthesized in this book, allowing a detailed understanding of the many challenges ahead and a roadmap for human exploration far beyond our solar system. While the road ahead is long and challenging, it provides the path to radically alter humanity's future.Related Link(s)

Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the text instills a working understanding of key statistical

Physics of Data Science and Machine Learning links fundamental concepts of physics to data science, machine learning, and artificial intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data mining, data science, and machine learning. It also explains how to integrate these techniques into the design of experiments, while exploring neural networks and machine learning, building on fundamental concepts of statistical and quantum mechanics. This book is a self-learning tool for physicists looking to learn how to utilize data science and machine learning in their research. It will also be of interest to computer scientists and applied mathematicians, alongside graduate students looking to understand the basic concepts and foundations of data science, machine learning, and artificial intelligence. Although specifically written for physicists, it will also help provide non-physicists with an opportunity to understand the fundamental concepts from a physics perspective to aid in the development of new and innovative machine learning and artificial intelligence tools. Key Features: Introduces the design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt, and adapt. Free from endless derivations; instead, equations are presented and it is explained strategically why it is imperative to use them and how they will help in the task at hand. Illustrations and simple explanations help readers visualize and absorb the difficult-to-understand concepts. Ijaz A. Rauf is an adjunct professor at the School of Graduate Studies, York University, Toronto, Canada. He is also an associate researcher at Ryerson University, Toronto, Canada and president of the Eminent-Tech Corporation, Bradford, ON, Canada.

Data has become a valuable asset like never before. Today the challenge is not a shortage of data but the need for techniques and methods capable enough to be able to glean valuable insights from the fast-flowing mass of Big Data. This new volume, Handbook of Research for Big Data: Concepts and Techniques, helps to meet the challenge of managing and using Big Data by presenting new research on various technological advances in the field. The chapters in the book present information on important applications, concepts, and technologies for Big Data in the present industry and market scenario. It looks at research domain issues and their solutions as well as various research case studies, research plans, methodologies, and related data sets for the four Vs: volume, velocity, variety, and veracity. Chapters discuss Big Data in governance, transportation, disaster management, epidemiology, and more. The book covers design and analysis of reconfigurable computing of SoC for IoT, data mining techniques and applications, the use of natural language processing in big data, and more. The volume is a valuable resource for researchers from both academia and industry to learn about and enhance their knowledge and skills in the broad area of big data computing and applications.

This book discusses the interplay between statistics, data science, machine learning and artificial intelligence, with a focus on environmental science, the natural sciences, and technology. It covers the state of the art from both a theoretical and a practical viewpoint and describes how to successfully apply machine learning methods, demonstrating the benefits of statistics for modeling and analyzing high-dimensional and big data. The book’s expert contributions include theoretical studies of machine learning methods, expositions of general methodologies for sound statistical analyses of data as well as novel approaches to modeling and analyzing data for specific problems and areas. In terms of applications, the contributions deal with data as arising in industrial quality control, autonomous driving, transportation and traffic, chip manufacturing, photovoltaics, football, transmission of infectious diseases, Covid-19 and public health. The book will appeal to statisticians and data scientists, as well as engineers and computer scientists working in related fields or applications.