Numerical Approximation of the Magnetoquasistatic Model with Uncertainties, R?mer
Автор: Andreas Rauh; Ekaterina Auer Название: Modeling, Design, and Simulation of Systems with Uncertainties ISBN: 3642268560 ISBN-13(EAN): 9783642268564 Издательство: Springer Рейтинг: Цена: 26120.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This three-fold contribution to the field covers both theory and current research in algorithmic approaches to uncertainty handling, real-life applications such as robotics and biomedical engineering, and fresh approaches to reliably implementing software.
Автор: N.V. Banichuk; Pekka Neittaanm?ki Название: Structural Optimization with Uncertainties ISBN: 9400731116 ISBN-13(EAN): 9789400731110 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This text details new ways to formulate problems of structural optimization with incomplete information and solution techniques. It is mostly devoted to the minimax approach using worst case scenario, but it also covers mixed probabilistic guaranteed approach.
Описание: This research monograph brings together, for the first time, the varied literature on Yosida approximations of stochastic differential equations (SDEs) in infinite dimensions and their applications into a single cohesive work. The author provides a clear and systematic introduction to the Yosida approximation method and justifies its power by presenting its applications in some practical topics such as stochastic stability and stochastic optimal control. The theory assimilated spans more than 35 years of mathematics, but is developed slowly and methodically in digestible pieces.The book begins with a motivational chapter that introduces the reader to several different models that play recurring roles throughout the book as the theory is unfolded, and invites readers from different disciplines to see immediately that the effort required to work through the theory that follows is worthwhile. From there, the author presents the necessary prerequisite material, and then launches the reader into the main discussion of the monograph, namely, Yosida approximations of SDEs, Yosida approximations of SDEs with Poisson jumps, and their applications. Most of the results considered in the main chapters appear for the first time in a book form, and contain illustrative examples on stochastic partial differential equations. The key steps are included in all proofs, especially the various estimates, which help the reader to get a true feel for the theory of Yosida approximations and their use.This work is intended for researchers and graduate students in mathematics specializing in probability theory and will appeal to numerical analysts, engineers, physicists and practitioners in finance who want to apply the theory of stochastic evolution equations. Since the approach is based mainly in semigroup theory, it is amenable to a wide audience including non-specialists in stochastic processes.
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