Scientists and engineers routinely use graphical reprsentations to organize their thoughts and as parts of the process of solving verbally presented problems. In a cross-disciplinary study that has important implications for research in artificial intelligence and complex nonlinear dynamics, Yip shows how to automate key aspects of this style of reasoning. He domonstrates the basic feasibility of intelligently guided numerical experimentation in a computational theory and a system for implementing the theory. The system, called KAM, is the first computer system that can intelligently guide numerical experimentation and interpret the numerical results in high-level, conceptual terms. KAM's ability to steer numerical experiments arises from the fact taht it not only produces images by also looks at the pictures it draws to guide its own actions. By combining techniques from computer vision with sophisticated dynamical invariants, KAM is able to exploit mathematical knowledge, encoded as visual consistency constraints on the phase space and parameter space, to constrain its search for interesting behaviours. The approach is applied to Hamiltonian systems with two degrees of freedom, an area that is currently of great physical interest, and its power is tested in a difficult problem in hydrodynamics, for which KAM helps derive previously unknown publishable results.
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Scientists and engineers routinely use graphical reprsentations to organize their thoughts and as parts of the process of solving verbally presented problems. In a cross-disciplinary study that has important implications for research in artificial intelligence and complex nonlinear dynamics, Yip shows how to automate key aspects of this style of reasoning. He domonstrates the basic feasibility of intelligently guided numerical experimentation in a computational theory and a system for implementing the theory. The system, called KAM, is the first computer system that can intelligently guide numerical experimentation and interpret the numerical results in high-level, conceptual terms. KAM's ability to steer numerical experiments arises from the fact taht it not only produces images by also looks at the pictures it draws to guide its own actions. By combining techniques from computer vision with sophisticated dynamical invariants, KAM is able to exploit mathematical knowledge, encoded as visual consistency constraints on the phase space and parameter space, to constrain its search for interesting behaviours. The approach is applied to Hamiltonian systems with two degrees of freedom, an area that is currently of great physical interest, and its power is tested in a difficult problem in hydrodynamics, for which KAM helps derive previously unknown publishable results.
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Anbieter: Wonder Book, Frederick, MD, USA
Zustand: Very Good. Very Good condition. Very Good dust jacket. A copy that may have a few cosmetic defects. May also contain light spine creasing or a few markings such as an owner's name, short gifter's inscription or light stamp. Artikel-Nr. N21J-00381
Anbieter: Zubal-Books, Since 1961, Cleveland, OH, USA
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Hardcover. Zustand: Très bon. Ancien livre de bibliothèque. Couverture différente. Edition 1991. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Different cover. Edition 1991. Ammareal gives back up to 15% of this item's net price to charity organizations. Artikel-Nr. E-592-912
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Anbieter: Kloof Booksellers & Scientia Verlag, Amsterdam, Niederlande
Zustand: as new. Cambridge, MA. : The MIT Press, 1991. Hardcover. Dustjacket. 242 pp. Scientists and engineers routinely use graphical representations to organize their thoughts and as parts of the process of solving verbally presented problems. In a cross-disciplinary study that has important implications for research in artificial intelligence and complex nonlinear dynamics, Yip shows how to automate key aspects of this style of reasoning. He demonstrates the basic feasibility of intelligently guided numerical experimentation in a computational theory and a system for implementing the theory. The system, called KAM, is the first computer system that can intelligently guide numerical experimentation and interpret the numerical results in high-level, conceptual terms. KAM's ability to steer numerical experiments arises from the fact that it not only produces images but also looks at the pictures it draws to guide its own actions. By combining techniques from computer vision with sophisticated dynamical invariants, KAM is able to exploit mathematical knowledge, encoded as visual consistency constraints on the phase space and parameter space, to constrain its search for interesting behaviors. The approach is applied to Hamiltonian systems with two degrees of freedom, an area that is currently of great physical interest, and its power is tested in a difficult problem in hydrodynamics, for which KAM helps derive previously unknown publishable results. Condition : as new copy. ISBN 9780262240345. Keywords : Computer studies, Hamiltonian systems. Artikel-Nr. 284157
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