发牢骚
发表于 2025-3-25 03:35:06
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wangle
发表于 2025-3-25 08:17:28
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混沌
发表于 2025-3-25 11:43:08
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评论性
发表于 2025-3-25 16:31:38
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Crayon
发表于 2025-3-25 20:50:43
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dictator
发表于 2025-3-26 03:38:01
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
brachial-plexus
发表于 2025-3-26 08:22:12
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.
insurrection
发表于 2025-3-26 09:14:05
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柳树；枯黄
发表于 2025-3-26 15:50:23
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Explicate
发表于 2025-3-26 19:21:20
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