Results 21 to 30 of about 3,175,086 (283)
Model Order Reduction of Non-Linear Magnetostatic Problems Based on POD and DEI Methods [PDF]
, 2014 In the domain of numerical computation, Model Order Reduction approaches are more and more frequently applied in mechanics and have shown their efficiency in terms of reduction of computation time and memory storage requirements. One of these approaches,
CLENET, Stephane, HENNERON, Thomas
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Linear Interpolation Method for Adversarial Attack [PDF]
Jisuanji kexueDeep neural networks exhibit significant vulnerability in the face of adversarial examples and are prone to attacks.The construction of adversarial examples can be abstracted as an optimization problem that maximizes the objective function.How-ever ...
CHEN Jun, ZHOU Qiang, BAO Lei, TAO Qing
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Distance in spatial interpolation of daily rain gauge data [PDF]
, 2005 Spatial interpolation of rain gauge data is important in forcing of hydrological simulations or evaluation of weather predictions, for example. The spatial density of available data sites is often changing with time.
Ahrens, B., Ahrens, Bodo
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Bayesian interpolation with deep linear networks
Proceedings of the National Academy of Sciences, 2023 Characterizing how neural network depth, width, and dataset size jointly impact model quality is a central problem in deep learning theory. We give here a complete solution in the special case of linear networks with output dimension one trained using zero noise Bayesian inference with Gaussian weight priors and mean squared error as a negative log ...
Boris Hanin, Alexander Zlokapa
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Advances in Mechanical Engineering, 2013 In general, proper orthogonal decomposition (POD) method is used to deal with single-parameter problems in engineering practice, and the linear interpolation is employed to establish the reduced model.
Xiaolong Wang +7 more
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A FAST MORPHING-BASED INTERPOLATION FOR MEDICAL IMAGES: APPLICATION TO CONFORMAL RADIOTHERAPY
Image Analysis and Stereology, 2011 A method is presented for fast interpolation between medical images. The method is intended for both slice and projective interpolation. It allows offline interpolation between neighboring slices in tomographic data.
Hussein Atoui +2 more
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Some applications of Sigmoid functions [PDF]
Iranian Journal of Numerical Analysis and Optimization, 2021 In numerical analysis, the process of fitting a function via given data is called interpolation. Interpolation has many applications in engineering and science.
M. A. Jafari, A. Aminataei
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Analytic Kramer kernels, Lagrange-type interpolation series and de Branges spaces [PDF]
, 2011 The classical Kramer sampling theorem provides a method for obtaining orthogonal sampling formulas. In particular, when the involved kernel is analytic in the sampling parameter it can be stated in an abstract setting of reproducing kernel Hilbert spaces
Hernández-Medina, Miguel A. +7 more
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Sparse-view tomography via displacement function interpolation
Visual Computing for Industry, Biomedicine, and Art, 2019 Sparse-view tomography has many applications such as in low-dose computed tomography (CT). Using under-sampled data, a perfect image is not expected.
Gengsheng L. Zeng
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Accuracy of compact-stencil interpolation algorithms for unstructured mesh finite volume solver
Heliyon, 2021 This study considers the accuracy of cell-to-face centre interpolation of convected quantities in unstructured finite volume meshes with cell-centred storage of variables.
Adek Tasri, Anita Susilawati
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