Download or read book Accelerating Scientific Discovery written by and published by DIANE Publishing. This book was released on with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Knowledge Guided Machine Learning written by Anuj Karpatne and published by CRC Press. This book was released on 2022-08-15 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML
Download or read book Reinventing Discovery written by Michael Nielsen and published by Princeton University Press. This book was released on 2020-04-07 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Reinventing Discovery argues that we are in the early days of the most dramatic change in how science is done in more than 300 years. This change is being driven by new online tools, which are transforming and radically accelerating scientific discovery"--
Download or read book Accelerating Scientific Discovery Through Computation and Visualization written by and published by DIANE Publishing. This book was released on with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Accelerating Discovery written by Scott Spangler and published by CRC Press. This book was released on 2015-09-18 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unstructured Mining Approaches to Solve Complex Scientific ProblemsAs the volume of scientific data and literature increases exponentially, scientists need more powerful tools and methods to process and synthesize information and to formulate new hypotheses that are most likely to be both true and important. Accelerating Discovery: Mining Unstructu
Download or read book Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment Big Data Modeling and Simulation written by Kothe Doug and published by Springer Nature. This book was released on 2023-01-17 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 22nd Smoky Mountains Computational Sciences and Engineering Conference on Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation, SMC 2022, held virtually, during August 23–25, 2022. The 24 full papers included in this book were carefully reviewed and selected from 74 submissions. They were organized in topical sections as follows: foundational methods enabling science in an integrated ecosystem; science and engineering applications requiring and motivating an integrated ecosystem; systems and software advances enabling an integrated science and engineering ecosystem; deploying advanced technologies for an integrated science and engineering ecosystem; and scientific data challenges.
Download or read book Failure written by Stuart Firestein and published by Oxford University Press, USA. This book was released on 2016 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: In his sequel to Ignorance (Oxford University Press, 2012), Stuart Firestein shows us that the scientific enterprise is riddled with mistakes and errors - and that this is a good thing! Failure: Why Science Is So Successful delves into the origins of scientific research as a process that relies upon trial and error, one which inevitably results in a hefty dose of failure.
Download or read book Deep Learning for Physical Scientists written by Edward O. Pyzer-Knapp and published by John Wiley & Sons. This book was released on 2021-09-20 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the power of machine learning in the physical sciences with this one-stop resource from a leading voice in the field Deep Learning for Physical Scientists: Accelerating Research with Machine Learning delivers an insightful analysis of the transformative techniques being used in deep learning within the physical sciences. The book offers readers the ability to understand, select, and apply the best deep learning techniques for their individual research problem and interpret the outcome. Designed to teach researchers to think in useful new ways about how to achieve results in their research, the book provides scientists with new avenues to attack problems and avoid common pitfalls and problems. Practical case studies and problems are presented, giving readers an opportunity to put what they have learned into practice, with exemplar coding approaches provided to assist the reader. From modelling basics to feed-forward networks, the book offers a broad cross-section of machine learning techniques to improve physical science research. Readers will also enjoy: A thorough introduction to the basic classification and regression with perceptrons An exploration of training algorithms, including back propagation and stochastic gradient descent and the parallelization of training An examination of multi-layer perceptrons for learning from descriptors and de-noising data Discussions of recurrent neural networks for learning from sequences and convolutional neural networks for learning from images A treatment of Bayesian optimization for tuning deep learning architectures Perfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access. Perfect for academic and industrial research professionals in the physical sciences, em style="font-family: Calibri, sans-serif; font-size: 11pt;"Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access. This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including: •Basic classification and regression with perceptrons •Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training •Multi-Layer Perceptrons for learning from descriptors, and de-noising data •Recurrent neural networks for learning from sequences •Convolutional neural networks for learning from images •Bayesian optimization for tuning deep learning architectures Each of these areas has direct application to physical science research, and by the end of the book, the reader should feel comfortable enough to select the methodology which is best for their situation, and be able to implement and interpret outcome of the deep learning model. The book is designed to teach researchers to think in new ways, providing them with new avenues to attack problems, and avoid roadblocks within their research. This is achieved through the inclusion of case-study like problems at the end of each chapter, which will give the reader a chance to practice what they have just learnt in a close-to-real-world setting, with example ‘solutions’ provided through an online resource. Market Description This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including: • Basic classification and regression with perceptrons • Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training • Multi-Layer Perceptrons for learning from descriptors, and de-noising data • Recurrent neural networks for learning from sequences • Convolutional neural networks for learning from images • Bayesian optimization for tuning deep learning architectures Each of these areas has direct application to physical science research, and by the end of the book, the reader should feel comfortable enough to select the methodology which is best for their situation, and be able to implement and interpret outcome of the deep learning model. The book is designed to teach researchers to think in new ways, providing them with new avenues to attack problems, and avoid roadblocks within their research. This is achieved through the inclusion of case-study like problems at the end of each chapter, which will give the reader a chance to practice what they have just learnt in a close-to-real-world setting, with example ‘solutions’ provided through an online resource.
Download or read book Driving Scientific and Engineering Discoveries Through the Convergence of HPC Big Data and AI written by Jeffrey Nichols and published by Springer Nature. This book was released on 2020-12-22 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised selected papers of the 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, held in Oak Ridge, TN, USA*, in August 2020. The 36 full papers and 1 short paper presented were carefully reviewed and selected from a total of 94 submissions. The papers are organized in topical sections of computational applications: converged HPC and artificial intelligence; system software: data infrastructure and life cycle; experimental/observational applications: use cases that drive requirements for AI and HPC convergence; deploying computation: on the road to a converged ecosystem; scientific data challenges. *The conference was held virtually due to the COVID-19 pandemic.
Download or read book Accelerating the Development of Biomarkers for Drug Safety written by Institute of Medicine and published by National Academies Press. This book was released on 2009-07-20 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biomarkers can be defined as indicators of any biologic state, and they are central to the future of medicine. As the cost of developing drugs has risen in recent years, reducing the number of new drugs approved for use, biomarker development may be a way to cut costs, enhance safety, and provide a more focused and rational pathway to drug development. On October 24, 2008, the IOM's Forum on Drug Discovery, Development, and Translation held "Assessing and Accelerating Development of Biomarkers for Drug Safety," a one-day workshop, summarized in this volume, on the value of biomarkers in helping to determine drug safety during development.
Download or read book Neither Physics nor Chemistry written by Kostas Gavroglu and published by MIT Press. This book was released on 2011-10-07 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: The evolution of a discipline at the intersection of physics, chemistry, and mathematics. Quantum chemistry—a discipline that is not quite physics, not quite chemistry, and not quite applied mathematics—emerged as a field of study in the 1920s. It was referred to by such terms as mathematical chemistry, subatomic theoretical chemistry, molecular quantum mechanics, and chemical physics until the community agreed on the designation of quantum chemistry. In Neither Physics Nor Chemistry, Kostas Gavroglu and Ana Simões examine the evolution of quantum chemistry into an autonomous discipline, tracing its development from the publication of early papers in the 1920s to the dramatic changes brought about by the use of computers in the 1970s. The authors focus on the culture that emerged from the creative synthesis of the various traditions of chemistry, physics, and mathematics. They examine the concepts, practices, languages, and institutions of this new culture as well as the people who established it, from such pioneers as Walter Heitler and Fritz London, Linus Pauling, and Robert Sanderson Mulliken, to later figures including Charles Alfred Coulson, Raymond Daudel, and Per-Olov Löwdin. Throughout, the authors emphasize six themes: epistemic aspects and the dilemmas caused by multiple approaches; social issues, including academic politics, the impact of textbooks, and the forging of alliances; the contingencies that arose at every stage of the developments in quantum chemistry; the changes in the field when computers were available to perform the extraordinarily cumbersome calculations required; issues in the philosophy of science; and different styles of reasoning.
Download or read book Age of Discovery written by Ian Goldin and published by Macmillan + ORM. This book was released on 2016-05-24 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present is a contest between the bright and dark sides of discovery. To avoid being torn apart by its stresses, we need to recognize the fact—and gain courage and wisdom from the past. Age of Discovery shows how. Now is the best moment in history to be alive, but we have never felt more anxious or divided. Human health, aggregate wealth and education are flourishing. Scientific discovery is racing forward. But the same global flows of trade, capital, people and ideas that make gains possible for some people deliver big losses to others—and make us all more vulnerable to one another. Business and science are working giant revolutions upon our societies, but our politics and institutions evolve at a much slower pace. That’s why, in a moment when everyone ought to be celebrating giant global gains, many of us are righteously angry at being left out and stressed about where we’re headed. To make sense of present shocks, we need to step back and recognize: we’ve been here before. The first Renaissance, the time of Columbus, Copernicus, Gutenberg and others, likewise redrew all maps of the world, democratized communication and sparked a flourishing of creative achievement. But their world also grappled with the same dark side of rapid change: social division, political extremism, insecurity, pandemics and other unintended consequences of discovery. Now is the second Renaissance. We can still flourish—if we learn from the first.
Download or read book The Extravagant Universe written by Robert P. Kirshner and published by Princeton University Press. This book was released on 2016-09-13 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Extravagant Universe tells the story of a remarkable adventure of scientific discovery. One of the world's leading astronomers, Robert Kirshner, takes readers inside a lively research team on the quest that led them to an extraordinary cosmological discovery: the expansion of the universe is accelerating under the influence of a dark energy that makes space itself expand. In addition to sharing the story of this exciting discovery, Kirshner also brings the science up-to-date in a new epilogue. He explains how the idea of an accelerating universe--once a daring interpretation of sketchy data--is now the standard assumption in cosmology today. This measurement of dark energy--a quality of space itself that causes cosmic acceleration--points to a gaping hole in our understanding of fundamental physics. In 1917, Einstein proposed the "cosmological constant" to explain a static universe. When observations proved that the universe was expanding, he cast this early form of dark energy aside. But recent observations described first-hand in this book show that the cosmological constant--or something just like it--dominates the universe's mass and energy budget and determines its fate and shape. Warned by Einstein's blunder, and contradicted by the initial results of a competing research team, Kirshner and his colleagues were reluctant to accept their own result. But, convinced by evidence built on their hard-earned understanding of exploding stars, they announced their conclusion that the universe is accelerating in February 1998. Other lines of inquiry and parallel supernova research now support a new synthesis of a cosmos dominated by dark energy but also containing several forms of dark matter. We live in an extravagant universe with a surprising number of essential ingredients: the real universe we measure is not the simplest one we could imagine.
Download or read book Informatics for Materials Science and Engineering Data Driven Discovery for Accelerated Experimentation and Application written by Krishna Rajan and published by Butterworth-Heinemann. This book was released on 2017-11-13 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: Materials informatics: a hot topic area in materials science, aims to combine traditionally bio-led informatics with computational methodologies, supporting more efficient research by identifying strategies for time- and cost-effective analysis. The discovery and maturation of new materials has been outpaced by the thicket of data created by new combinatorial and high throughput analytical techniques. The elaboration of this "quantitative avalanche" and the resulting complex, multi-factor analyses required to understand it means that interest, investment, and research are revisiting informatics approaches as a solution. This work, from Krishna Rajan, the leading expert of the informatics approach to materials, seeks to break down the barriers between data management, quality standards, data mining, exchange, and storage and analysis, as a means of accelerating scientific research in materials science. This solutions-based reference synthesizes foundational physical, statistical, and mathematical content with emerging experimental and real-world applications, for interdisciplinary researchers and those new to the field. Identifies and analyzes interdisciplinary strategies (including combinatorial and high throughput approaches) that accelerate materials development cycle times and reduces associated costs Mathematical and computational analysis aids formulation of new structure-property correlations among large, heterogeneous, and distributed data sets Practical examples, computational tools, and software analysis benefits rapid identification of critical data and analysis of theoretical needs for future problems "
Download or read book Strategies for Team Science Success written by Kara L. Hall and published by Springer Nature. This book was released on 2019-11-13 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collaborations that integrate diverse perspectives are critical to addressing many of our complex scientific and societal problems. Yet those engaged in cross-disciplinary team science often face institutional barriers and collaborative challenges. Strategies for Team Science Success offers readers a comprehensive set of actionable strategies for reducing barriers and overcoming challenges and includes practical guidance for how to implement effective team science practices. More than 100 experts--including scientists, administrators, and funders from a wide range of disciplines and professions-- explain evidence-based principles, highlight state-of the-art strategies, tools, and resources, and share first-person accounts of how they’ve applied them in their own successful team science initiatives. While many examples draw from cross-disciplinary team science initiatives in the health domain, the handbook is designed to be useful across all areas of science. Strategies for Team Science Success will inspire and enable readers to embrace cross-disciplinary team science, by articulating its value for accelerating scientific progress, and by providing practical strategies for success. Scientists, administrators, funders, and others engaged in team science will also leave equipped to develop new policies and practices needed to keep pace in our rapidly changing scientific landscape. Scholars across the Science of Team Science (SciTS), management, organizational, behavioral and social sciences, public health, philosophy, and information technology, among other areas of scholarship, will find inspiration for new research directions to continue advancing cross-disciplinary team science.
Download or read book Ambient Intelligence for Scientific Discovery written by Yang Cai and published by Springer Science & Business Media. This book was released on 2005-02-16 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many difficult scientific discovery tasks can only be solved in interactive ways, by combining intelligent computing techniques with intuitive and adaptive user interfaces. It is inevitable to use human intelligence in scientific discovery systems: human eyes can capture complex patterns and relationships, along with detecting the exceptional cases in a data set; the human brain can easily manipulate perceptions to make decisions. Ambient intelligence is about this kind of ubiquitous and autonomous human interaction with information. Scientific discovery is a process of creative perception and communication, dealing with questions like: how do we significantly reduce information while maintaining meaning, or how do we extract patterns from massive data and growing data resources. Originating from the SIGCHI Workshop on Ambient Intelligence for Scientific Discovery, this state-of-the-art survey is organized in three parts: new paradigms in scientific discovery, ambient cognition, and ambient intelligence systems. Many chapters share common features such as interaction, vision, language, and biomedicine.
Download or read book Scientific Discovery in the Social Sciences written by Mark Addis and published by Springer Nature. This book was released on 2019-09-12 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume offers selected papers exploring issues arising from scientific discovery in the social sciences. It features a range of disciplines including behavioural sciences, computer science, finance, and statistics with an emphasis on philosophy. The first of the three parts examines methods of social scientific discovery. Chapters investigate the nature of causal analysis, philosophical issues around scale development in behavioural science research, imagination in social scientific practice, and relationships between paradigms of inquiry and scientific fraud. The next part considers the practice of social science discovery. Chapters discuss the lack of genuine scientific discovery in finance where hypotheses concern the cheapness of securities, the logic of scientific discovery in macroeconomics, and the nature of that what discovery with the Solidarity movement as a case study. The final part covers formalising theories in social science. Chapters analyse the abstract model theory of institutions as a way of representing the structure of scientific theories, the semi-automatic generation of cognitive science theories, and computational process models in the social sciences. The volume offers a unique perspective on scientific discovery in the social sciences. It will engage scholars and students with a multidisciplinary interest in the philosophy of science and social science.
