10,167 research outputs found
GPU-accelerated discontinuous Galerkin methods on hybrid meshes
We present a time-explicit discontinuous Galerkin (DG) solver for the
time-domain acoustic wave equation on hybrid meshes containing vertex-mapped
hexahedral, wedge, pyramidal and tetrahedral elements. Discretely energy-stable
formulations are presented for both Gauss-Legendre and Gauss-Legendre-Lobatto
(Spectral Element) nodal bases for the hexahedron. Stable timestep restrictions
for hybrid meshes are derived by bounding the spectral radius of the DG
operator using order-dependent constants in trace and Markov inequalities.
Computational efficiency is achieved under a combination of element-specific
kernels (including new quadrature-free operators for the pyramid), multi-rate
timestepping, and acceleration using Graphics Processing Units.Comment: Submitted to CMAM
Time-causal and time-recursive spatio-temporal receptive fields
We present an improved model and theory for time-causal and time-recursive
spatio-temporal receptive fields, based on a combination of Gaussian receptive
fields over the spatial domain and first-order integrators or equivalently
truncated exponential filters coupled in cascade over the temporal domain.
Compared to previous spatio-temporal scale-space formulations in terms of
non-enhancement of local extrema or scale invariance, these receptive fields
are based on different scale-space axiomatics over time by ensuring
non-creation of new local extrema or zero-crossings with increasing temporal
scale. Specifically, extensions are presented about (i) parameterizing the
intermediate temporal scale levels, (ii) analysing the resulting temporal
dynamics, (iii) transferring the theory to a discrete implementation, (iv)
computing scale-normalized spatio-temporal derivative expressions for
spatio-temporal feature detection and (v) computational modelling of receptive
fields in the lateral geniculate nucleus (LGN) and the primary visual cortex
(V1) in biological vision.
We show that by distributing the intermediate temporal scale levels according
to a logarithmic distribution, we obtain much faster temporal response
properties (shorter temporal delays) compared to a uniform distribution.
Specifically, these kernels converge very rapidly to a limit kernel possessing
true self-similar scale-invariant properties over temporal scales, thereby
allowing for true scale invariance over variations in the temporal scale,
although the underlying temporal scale-space representation is based on a
discretized temporal scale parameter.
We show how scale-normalized temporal derivatives can be defined for these
time-causal scale-space kernels and how the composed theory can be used for
computing basic types of scale-normalized spatio-temporal derivative
expressions in a computationally efficient manner.Comment: 39 pages, 12 figures, 5 tables in Journal of Mathematical Imaging and
Vision, published online Dec 201
Bayesian Inference of Log Determinants
The log-determinant of a kernel matrix appears in a variety of machine
learning problems, ranging from determinantal point processes and generalized
Markov random fields, through to the training of Gaussian processes. Exact
calculation of this term is often intractable when the size of the kernel
matrix exceeds a few thousand. In the spirit of probabilistic numerics, we
reinterpret the problem of computing the log-determinant as a Bayesian
inference problem. In particular, we combine prior knowledge in the form of
bounds from matrix theory and evidence derived from stochastic trace estimation
to obtain probabilistic estimates for the log-determinant and its associated
uncertainty within a given computational budget. Beyond its novelty and
theoretic appeal, the performance of our proposal is competitive with
state-of-the-art approaches to approximating the log-determinant, while also
quantifying the uncertainty due to budget-constrained evidence.Comment: 12 pages, 3 figure
A high-order Nystrom discretization scheme for boundary integral equations defined on rotationally symmetric surfaces
A scheme for rapidly and accurately computing solutions to boundary integral
equations (BIEs) on rotationally symmetric surfaces in R^3 is presented. The
scheme uses the Fourier transform to reduce the original BIE defined on a
surface to a sequence of BIEs defined on a generating curve for the surface. It
can handle loads that are not necessarily rotationally symmetric. Nystrom
discretization is used to discretize the BIEs on the generating curve. The
quadrature is a high-order Gaussian rule that is modified near the diagonal to
retain high-order accuracy for singular kernels. The reduction in
dimensionality, along with the use of high-order accurate quadratures, leads to
small linear systems that can be inverted directly via, e.g., Gaussian
elimination. This makes the scheme particularly fast in environments involving
multiple right hand sides. It is demonstrated that for BIEs associated with the
Laplace and Helmholtz equations, the kernel in the reduced equations can be
evaluated very rapidly by exploiting recursion relations for Legendre
functions. Numerical examples illustrate the performance of the scheme; in
particular, it is demonstrated that for a BIE associated with Laplace's
equation on a surface discretized using 320,800 points, the set-up phase of the
algorithm takes 1 minute on a standard laptop, and then solves can be executed
in 0.5 seconds.Comment: arXiv admin note: substantial text overlap with
arXiv:1012.56301002.200
An explicit kernel-split panel-based Nystr\"om scheme for integral equations on axially symmetric surfaces
A high-order accurate, explicit kernel-split, panel-based, Fourier-Nystr\"om
discretization scheme is developed for integral equations associated with the
Helmholtz equation in axially symmetric domains. Extensive incorporation of
analytic information about singular integral kernels and on-the-fly computation
of nearly singular quadrature rules allow for very high achievable accuracy,
also in the evaluation of fields close to the boundary of the computational
domain.Comment: 30 pages, 5 figures, errata correcte
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