171 research outputs found
Local RBF approximation for scattered data fitting with bivariate splines
In this paper we continue our earlier research [4] aimed at developing effcient methods of local approximation suitable for the first stage of a spline based two-stage scattered data fitting algorithm. As an improvement to the pure polynomial local approximation method used in [5], a hybrid polynomial/radial basis scheme was considered in [4], where the local knot locations for the RBF terms were selected using a greedy knot insertion algorithm. In this paper standard radial local approximations based on interpolation or least squares are considered and a faster procedure is used for knot selection, signicantly reducing the computational cost of the method. Error analysis of the method and numerical results illustrating its performance are given
Greedy kernel methods for accelerating implicit integrators for parametric ODEs
We present a novel acceleration method for the solution of parametric ODEs by
single-step implicit solvers by means of greedy kernel-based surrogate models.
In an offline phase, a set of trajectories is precomputed with a high-accuracy
ODE solver for a selected set of parameter samples, and used to train a kernel
model which predicts the next point in the trajectory as a function of the last
one. This model is cheap to evaluate, and it is used in an online phase for new
parameter samples to provide a good initialization point for the nonlinear
solver of the implicit integrator. The accuracy of the surrogate reflects into
a reduction of the number of iterations until convergence of the solver, thus
providing an overall speedup of the full simulation. Interestingly, in addition
to providing an acceleration, the accuracy of the solution is maintained, since
the ODE solver is still used to guarantee the required precision. Although the
method can be applied to a large variety of solvers and different ODEs, we will
present in details its use with the Implicit Euler method for the solution of
the Burgers equation, which results to be a meaningful test case to demonstrate
the method's features
On thin plate spline interpolation
We present a simple, PDE-based proof of the result [M. Johnson, 2001] that
the error estimates of [J. Duchon, 1978] for thin plate spline interpolation
can be improved by . We illustrate that -matrix
techniques can successfully be employed to solve very large thin plate spline
interpolation problem
Reproducing Kernels of Generalized Sobolev Spaces via a Green Function Approach with Distributional Operators
In this paper we introduce a generalized Sobolev space by defining a
semi-inner product formulated in terms of a vector distributional operator
consisting of finitely or countably many distributional operators
, which are defined on the dual space of the Schwartz space. The types of
operators we consider include not only differential operators, but also more
general distributional operators such as pseudo-differential operators. We
deduce that a certain appropriate full-space Green function with respect to
now becomes a conditionally positive
definite function. In order to support this claim we ensure that the
distributional adjoint operator of is
well-defined in the distributional sense. Under sufficient conditions, the
native space (reproducing-kernel Hilbert space) associated with the Green
function can be isometrically embedded into or even be isometrically
equivalent to a generalized Sobolev space. As an application, we take linear
combinations of translates of the Green function with possibly added polynomial
terms and construct a multivariate minimum-norm interpolant to data
values sampled from an unknown generalized Sobolev function at data sites
located in some set . We provide several examples, such
as Mat\'ern kernels or Gaussian kernels, that illustrate how many
reproducing-kernel Hilbert spaces of well-known reproducing kernels are
isometrically equivalent to a generalized Sobolev space. These examples further
illustrate how we can rescale the Sobolev spaces by the vector distributional
operator . Introducing the notion of scale as part of the
definition of a generalized Sobolev space may help us to choose the "best"
kernel function for kernel-based approximation methods.Comment: Update version of the publish at Num. Math. closed to Qi Ye's Ph.D.
thesis (\url{http://mypages.iit.edu/~qye3/PhdThesis-2012-AMS-QiYe-IIT.pdf}
On Fourier transforms of radial functions and distributions
We find a formula that relates the Fourier transform of a radial function on
with the Fourier transform of the same function defined on
. This formula enables one to explicitly calculate the
Fourier transform of any radial function in any dimension, provided one
knows the Fourier transform of the one-dimensional function and
the two-dimensional function . We prove analogous
results for radial tempered distributions.Comment: 12 page
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