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Titlebook: Systems Biology; Applications and Per P. Bringmann,E. C. Butcher,B. Weiss Conference proceedings 2007 Springer-Verlag Berlin Heidelberg 200

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H. V. Westerhoffhnique. This allows us to efficiently handle huge amounts of training data in moderate dimensions. Extensions of the basic method lead to space- and dimension-adaptive sparse grid algorithms. They become useful if the attractor is only located in a small part of the embedding space or if its dimensi
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H. Kitanoe necessary. We present numerical examples to show that we obtain not only competitive results with the interpolation on sparse grids but that we can even be better than the RB approximation if we are only interested in a rough but very fast approximation.
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C. A. Lieu,K. O. Ellistonnteed as a result of the wavelet basis having the Riesz property. This property provides an additional lower bound estimate for the wavelet coefficients that are used to guide the adaptive grid refinement, resulting in the sg-AWSCM requiring a significantly reduced number of deterministic simulation
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