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Dimensionality Reduction and Weight-Parameterized Neural NetworkSurrogates for Climate Models
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Improving multifidelity uncertainty propagation for cardiovascular applications through dimensionality reduction techniques
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Dimensionality Reduction for Quasi-Biennial Oscillation Data
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NeurAM: nonlinear dimensionality reduction for uncertainty quantification through neural active manifolds
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Stratified sampling based on nonlinear dimensionality reduction for multi-fidelity estimators
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Efficient data-driven surrogate modeling of high-speed aerodynamic field data using dimensionality reduction and neural networks
Presentation for USNCCM on work on non-intrusive reduced order models for aerothermal modeling.
Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction
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LANDSAT-D thematic mapper image dimensionality reduction and geometric correction accuracy
When principal component analysis of a subscene of a section of the Sacramento River showed lower correlation among the TM spectral components that were observed for the MSS spectral components, principal component analysis was applied to a LANDSAT 2 MSS subscene of the same area for comparison purposes. Correlation coefficient matrices indicate the pairwise similarity and correlation of the data for the spectral components. The principal components transformation matrix, indicates the weights applied to the original components to generate the transformed components. The first two TM components can be described as visible and near infrared. For the MSS data, the first transformed component is roughly the average of the four original components. The second transformed component is roughly the difference between the visible and infrared components. Tables show that 97.0% of the variance in an MSS image is contained in only two transformed components.
Evaluation of Near Singular Integrals for Computational Electromagnetics by Dimensionality Reduction
With the need for ever faster codes, a limiting factor that must be dealt with is the accurate yet efficient evaluation of interaction integrals between the more problematic near-field elements. Several recent works have together shown that all evaluations of source potential integrals and their derivatives for the most common bases and elements can be reduced to the evaluation of boundary line integrals; these can be evaluated by Gauss-Legendre quadrature, though integrand-smoothing transforms are often needed to accelerate their computation. In this paper, we modify the reported approach to eliminate cancellation errors in the line integral integrand, reinterpret the integral as a vertex function, and study the scalar potential integral form under the sinh transform and static subtraction acceleration methods.
INVARIANT SUBSPACES AND DIMENSIONAL REDUCTION FOR QUANTUM DYNAMICS.
Abstract not provided.
Dimensionality Reduction Algorithms for Big Nuclear Data Streams with Application to SCALE Microscopic Cross Sections
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Cosmological String Solutions by Dimensional Reduction
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Dimensionality reduction of cortical spiking networks ? quantifying the structure behind the chaos.
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STRESS-CONSTRAINED TOPOLOGY DESIGN OF FUNCTIONALLY GRADED LATTICE STRUCTURES WITH SPLINE-BASED DIMENSIONALITY REDUCTION
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Hyperspectral vegetation identification utilizing polynomial fitting for dimensionality reduction.
Abstract not provided.