Sentence examples for manifold formulation from inspiring English sources

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This approach allows the full potential of the invariant manifold formulation to be reached, and is suitably general for application to a wide variety of non-linear systems.

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The theory of Riemannian manifolds offers foundations for a rigorous formulation of boundary value problems that are often faced in many engineering applications.

Using the manifold decomposition technique, we present two new formulations of the steering vector in the presence of mutual coupling for sparse UCAs.

The assembly constructed for the current work included a SIA system, miniaturized fiber optic spectrometric devices and pumped-tubes (Figure 4). Figure 4 Schematic diagram of a SIA manifold constructed for diclofenac assay in pharmaceutical formulations.

But a Lie group is also a smooth manifold, as described in the supplement A Modern Formulation of Riemann's Theory: the set G can be represented patchwise by systems of real-valued (or alternatively complex-valued) coordinates, mutually linked by well-defined, differentiable coordinate transformations wherever their respective patches overlap.

For L∞-functions on a (closed) compact Riemannian manifold, the noncommutative residue and the Dixmier trace formulation of the noncommutative integral are shown to equate to a multiple of the Lebesgue integral.

Based on the manifold decomposition technique, we will present two new formulations of the steering vector in the presence of mutual coupling for sparse UCAs.

Two formulations of the approach are shown: Manifold Generated from PCA (MG-PCA), based on a global analysis, and Manifold Generated from Local PCA (MG-L-PCA), based on performing the PCA analysis locally.

Analogous to an eigenvalue analysis of the Intrinsic-Low-Dimensional-Manifold method, the present study provides a rigorous mathematical formulation for flamelet-based tabulation methods.

Our primary contributions in this paper are (1) a formulation for modeling gait subspaces on the Grassmann manifold, (2) a framework to integrate supervised and unsupervised GE techniques in the Grassmann manifold, (3) a method to incorporate sparse representation in the learning algorithms, and (4) extensive experiment to corroborate the proposed approach.

Reasons for the failure of these studies are manifold and approaches to improve the efficacy of such topical formulations are in the focus of current research [26].

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