Описание: Challenges long-standing linear notions of scientific progress, arguing that transformative ideas don`t arise from the day-to-day, gradual process of experimentation and data accumulation, but that revolutions in science, those breakthrough moments that disrupt accepted thinking and offer unanticipated ideas, occur outside of normal science.
Автор: Langtangen Hans Petter Название: A Primer on Scientific Programming with Python ISBN: 3662498863 ISBN-13(EAN): 9783662498866 Издательство: Springer Рейтинг: Цена: 4890.00 р. 6986.00-30% Наличие на складе: Есть (1 шт.) Описание: The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches 'Matlab-style' and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science.From the reviews: Langtangen … does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. … Summing Up: Highly recommended. F. H. Wild III, Choice, Vol. 47 (8), April 2010Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer.” John D. Cook, The Mathematical Association of America, September 2011This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science.Alex Small, IEEE, CiSE Vol. 14 (2), March?/April 2012 “This fourth edition is awonderful, inclusive textbook that covers pretty much everything one needs toknow to go from zero to fairly sophisticated scientific programming in Python…”Joan Horvath, Computing Reviews, March2015
Автор: Barseghyan, Hakob Название: Laws of scientific change ISBN: 3319175955 ISBN-13(EAN): 9783319175959 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book systematically creates a general descriptive theory of scientific change that explains the mechanics of changes in both scientific theories and the methods of their assessment.
Автор: Peter J. Riggs Название: Natural Kinds, Laws of Nature and Scientific Methodology ISBN: 0792342259 ISBN-13(EAN): 9780792342250 Издательство: Springer Рейтинг: Цена: 23053.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Issues covered in this collection of essays include: the nature of physical laws; the basic entities that we can expect the world to consist of; the problem of induction; the aim(s) of science; the discovery of natural laws through scientific methods; and natural necessity and its status.
Автор: Peter J. Riggs Название: Natural Kinds, Laws of Nature and Scientific Methodology ISBN: 9048147506 ISBN-13(EAN): 9789048147502 Издательство: Springer Рейтинг: Цена: 23053.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Hakob Barseghyan Название: The Laws of Scientific Change ISBN: 3319367862 ISBN-13(EAN): 9783319367866 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book systematically creates a general descriptive theory of scientific change that explains the mechanics of changes in both scientific theories and the methods of their assessment.
An analysis of two heuristic strategies for the development of mechanistic models, illustrated with historical examples from the life sciences.
In Discovering Complexity, William Bechtel and Robert Richardson examine two heuristics that guided the development of mechanistic models in the life sciences: decomposition and localization. Drawing on historical cases from disciplines including cell biology, cognitive neuroscience, and genetics, they identify a number of "choice points" that life scientists confront in developing mechanistic explanations and show how different choices result in divergent explanatory models. Describing decomposition as the attempt to differentiate functional and structural components of a system and localization as the assignment of responsibility for specific functions to specific structures, Bechtel and Richardson examine the usefulness of these heuristics as well as their fallibility -- the sometimes false assumption underlying them that nature is significantly decomposable and hierarchically organized.
When Discovering Complexity was originally published in 1993, few philosophers of science perceived the centrality of seeking mechanisms to explain phenomena in biology, relying instead on the model of nomological explanation advanced by the logical positivists (a model Bechtel and Richardson found to be utterly inapplicable to the examples from the life sciences in their study). Since then, mechanism and mechanistic explanation have become widely discussed. In a substantive new introduction to this MIT Press edition of their book, Bechtel and Richardson examine both philosophical and scientific developments in research on mechanistic models since 1993.
Автор: Anne Auger, Benjamin Doerr Название: Theory of randomized search heuristics ISBN: 9814282669 ISBN-13(EAN): 9789814282666 Издательство: World Scientific Publishing Рейтинг: Цена: 19008.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Randomized search heuristics such as evolutionary algorithms, genetic algorithms, evolution strategies, ant colony and particle swarm optimization turn out to be highly successful for optimization in practice. This book covers the theoretical developments in randomized search heuristics such as runtime analysis, drift analysis and convergence.
Описание: The informational nature of biological organization, at levels from the genetic and epigenetic to the cognitive and linguistic.
Автор: Popper, Karl Название: The Logic of Scientific Discovery ISBN: 0415278449 ISBN-13(EAN): 9780415278447 Издательство: Taylor&Francis Рейтинг: Цена: 3367.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: First published in English in 1959, Karl Popper`s "The Logic of Scientific Discovery" revolutionized contemporary thinking about science and knowledge and is one of the most widely read books about science written in the 20th century.
Автор: Feyerabend Название: Realism, Rationalism and Scientific Method ISBN: 0521316421 ISBN-13(EAN): 9780521316422 Издательство: Cambridge Academ Рейтинг: Цена: 4435.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Over the past 30 years of the 20th century, Paul Feyerabend has developed a distinctive and influentical approach to problems in the philosophy of science. The most important and seminal of his published essays are collected here.
Автор: Pincock Christopher Название: Mathematics and Scientific Representation ISBN: 0190201398 ISBN-13(EAN): 9780190201395 Издательство: Oxford Academ Рейтинг: Цена: 4830.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Mathematics plays a central role in much of contemporary science, but philosophers have struggled to understand what this role is or how significant it might be for mathematics and science. Pincock tackles this perennial question by asking how mathematics contributes to the success of our best scientific representations.
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