Global ETD Search
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Showing 1 to 7 of 7 for “"Truncated Singular Value Decomposition"”.
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Automated wavelet analysis of low resolution gamma-ray spectra and peak area uncertainty
… by the Moore-Penrose pseudo inversion or by truncated singular value decomposition (TSVD). The results are compared with those given by OriginLab and Gaussian fitting in MATLAB, which are consistent with each other, while TSVD is shown to be more accurate. The wavelet algorithm using TSVD for …
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Source distribution analysis of magnetic microscopy maps of geological samples
… better than unregularized least square methods, truncated singular value decomposition, and Tikhonov regularization using an identity matrix (minimum norm). Our study also gives insight regarding the benefit and cost of setting various constraints. Our findings are then tested on real …
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Seeing Beyond Limits with Physics-Informed Priors
… strategy handles ill-conditioning by applying truncated singular value decomposition to reduce rank deficiencies, followed by a Stable Diffusion refiner (SDEdit) plug-and-play prior that constrains reconstructions to valid image spaces. Simulations and passive non-line-of-sight imaging …
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Riemannian geometry of matrix manifolds for Lagrangian uncertainty quantification of stochastic fluid flows
… formulas are found for the differential of the truncated Singular Value Decomposition, of the Polar Decomposition, and of the eigenspaces of a time dependent symmetric matrix. Convergent gradient flows that achieve related algebraic operations are provided. A generalization of this framework to …
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An Implementation-Based Exploration of HAPOD: Hierarchical Approximate Proper Orthogonal Decomposition
Proper Orthogonal Decomposition (POD), combined with the Method of Snapshots and Galerkin projection, is a popular method for the model order reduction of nonlinear PDEs. The POD requires the left singular vectors from the singular value decomposition (SVD) of an n-by-m "snapshot matrix" S, each …
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Dynamical Reduced-Order Models for High-Dimensional Systems
… matrices. They asymptotically approximate the truncated singular value decomposition at a greatly reduced cost while guaranteeing convergence to the best low-rank approximation in a fixed number of iterations. From these retractions, we develop the dynamically orthogonal Runge-Kutta (DORK) …
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A Computational Framework for Assessing and Optimizing the Performance of Observational Networks in 4D-Var Data Assimilation
… estimates of model states and parameter values, and results in considerably improved computer simulations. The acquisition and use of observations in data assimilation raises several important scientific questions related to optimal sensor network design, quantification of data impact, …