2020
DOI: 10.1007/s42967-019-00057-2
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Discrete Vector Calculus and Helmholtz Hodge Decomposition for Classical Finite Difference Summation by Parts Operators

Abstract: In this article, discrete variants of several results from vector calculus are studied for classical finite difference summation by parts operators in two and three space dimensions. It is shown that existence theorems for scalar/vector potentials of irrotational/solenoidal vector fields cannot hold discretely because of grid oscillations, which are characterised explicitly. This results in a non-vanishing remainder associated to grid oscillations in the discrete Helmholtz Hodge decomposition. Nevertheless, it… Show more

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Cited by 10 publications
(11 citation statements)
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“…The legitimate question of orthogonality is discussed here by adopting the notations of figure (1) where n is the unit vector orthogonal to the surface S of the physical domain. Consider the two vectors f = ∇φ and g = ∇ × ψ and writing their inner product < f , g > given by orthogonal decomposition theorems [27], [28]:…”
Section: Boundary Conditions and Orthogonalitymentioning
confidence: 99%
“…The legitimate question of orthogonality is discussed here by adopting the notations of figure (1) where n is the unit vector orthogonal to the surface S of the physical domain. Consider the two vectors f = ∇φ and g = ∇ × ψ and writing their inner product < f , g > given by orthogonal decomposition theorems [27], [28]:…”
Section: Boundary Conditions and Orthogonalitymentioning
confidence: 99%
“…Nullspace consistency will be used as an additional required mimetic property. This novel property was introduced in [48] and has been a key factor in [18,38]. Here, 1 1 1 denotes the discrete grid function with value unity at every node.…”
Section: Remark 23mentioning
confidence: 99%
“…Therefore, The following technique has been used in [38] to analyze properties of SBP operators in space. Here, it will be used to create new SBP schemes in time.…”
Section: The New Schemesmentioning
confidence: 99%
“…Hence, an automated approach using machine learning techniques was used instead (Ostaszewski et al, 2020). Due to their distinct shape and the comparatively large number of wave event occurrences it was possible to train a neural network to detect possible candidates for wave events with a high precision around 80%.…”
Section: Instrumentation and Event Selectionmentioning
confidence: 99%