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Multiscale, Nonlinear and Adaptive Approximation II

Angela Kunoth (Hrsg.), Ronald DeVore (Hrsg.)

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ca. 160,49
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Springer Nature Switzerland img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik

Beschreibung

This book presents a collection of high-quality papers in applied and numerical mathematics, as well as approximation theory, all closely related to Wolfgang Dahmen’s scientific contributions. Compiled in honor of his 75th birthday, the papers are written by leading experts and cover topics including nonlinear approximation theory, numerical analysis of partial differential equations, learning theory, and electron microscopy. A unifying theme throughout the collection is the emphasis on a solid mathematical foundation, which serves as the basis for the most efficient numerical algorithms used to simulate complex phenomena.

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Schlagwörter

high-dimensional integrals, learning theory, theory on deep learning, adaptive, numerical schemes for PDE with parameters or randomness, tensor networks, nonlinear, sparse or adaptive approximation, nonlinear approximation theory, electron microscopy, multiscale, wavelet and reduced basis methods