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Data assimilation is an approach that combines observations and model output, with the objective of improving the latter. This book places data assimilation into the broader context of inverse problems and the theory, methods, and algorithms that are used for their solution. It provides a framework for, and insight into, the inverse problem nature of data assimilation, emphasizing why and not just how. Methods and diagnostics are emphasized, enabling readers to readily apply them to their own field of study. Readers will find a comprehensive guide that is accessible to nonexperts; numerous examples and diverse applications from a broad range of domains, including geophysics and geophysical flows, environmental acoustics, medical imaging, mechanical and biomedical engineering, economics and finance, and traffic control and urban planning; and the latest methods for advanced data assimilation, combining variational and statistical approaches.
Covers the state of the art in automatic differentiation theory and practice. Intended for computational scientists and engineers, this book aims to provide insight into effective strategies for using automatic differentiation for design optimization, sensitivity analysis, and uncertainty quantification.
Monthly. Papers presented at recent meeting held all over the world by scientific, technical, engineering and medical groups. Sources are meeting programs and abstract publications, as well as questionnaires. Arranged under 17 subject sections, 7 of direct interest to the life scientist. Full programs of meetings listed under sections. Entry gives citation number, paper title, name, mailing address, and any ordering number assigned. Quarterly and annual indexes to subjects, authors, and programs (not available in monthly issues).
The Current Index to Statistics (CIS) is a bibliographic index of publications in statistics, probability, and related fields.
Vols. for 1963- include as pt. 2 of the Jan. issue: Medical subject headings.