Neumann-Neumann decomposition plus mass lumping enables fast sampling of Gaussian random fields on metric graphs while preserving theoretical convergence rates of the underlying finite-element scheme.
The Annals of Statistics48(4) (Aug 2020)
2 Pith papers cite this work. Polarity classification is still indexing.
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A convolution process on a directed network provides a covariance model for SST that respects physical barriers and currents, used to identify thermal hot spots via Monte Carlo RCP projections.
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Efficient generation of Gaussian random fields on metric graphs via domain decomposition and mass matrix lumping
Neumann-Neumann decomposition plus mass lumping enables fast sampling of Gaussian random fields on metric graphs while preserving theoretical convergence rates of the underlying finite-element scheme.
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A Convolution Process for Sea Surface Temperature Hot-Spot Identification in the Mediterranean Sea
A convolution process on a directed network provides a covariance model for SST that respects physical barriers and currents, used to identify thermal hot spots via Monte Carlo RCP projections.