3,111 research outputs found
Maximum entropy properties of discrete-time first-order stable spline kernel
The first order stable spline (SS-1) kernel is used extensively in
regularized system identification. In particular, the stable spline estimator
models the impulse response as a zero-mean Gaussian process whose covariance is
given by the SS-1 kernel. In this paper, we discuss the maximum entropy
properties of this prior. In particular, we formulate the exact maximum entropy
problem solved by the SS-1 kernel without Gaussian and uniform sampling
assumptions. Under general sampling schemes, we also explicitly derive the
special structure underlying the SS-1 kernel (e.g. characterizing the
tridiagonal nature of its inverse), also giving to it a maximum entropy
covariance completion interpretation. Along the way similar maximum entropy
properties of the Wiener kernel are also given
Maximum Entropy Kernels for System Identification
A new nonparametric approach for system identification has been recently
proposed where the impulse response is modeled as the realization of a
zero-mean Gaussian process whose covariance (kernel) has to be estimated from
data. In this scheme, quality of the estimates crucially depends on the
parametrization of the covariance of the Gaussian process. A family of kernels
that have been shown to be particularly effective in the system identification
framework is the family of Diagonal/Correlated (DC) kernels. Maximum entropy
properties of a related family of kernels, the Tuned/Correlated (TC) kernels,
have been recently pointed out in the literature. In this paper we show that
maximum entropy properties indeed extend to the whole family of DC kernels. The
maximum entropy interpretation can be exploited in conjunction with results on
matrix completion problems in the graphical models literature to shed light on
the structure of the DC kernel. In particular, we prove that the DC kernel
admits a closed-form factorization, inverse and determinant. These results can
be exploited both to improve the numerical stability and to reduce the
computational complexity associated with the computation of the DC estimator.Comment: Extends results of 2014 IEEE MSC Conference Proceedings
(arXiv:1406.5706
Smoothed Particle Hydrodynamics and Magnetohydrodynamics
This paper presents an overview and introduction to Smoothed Particle
Hydrodynamics and Magnetohydrodynamics in theory and in practice. Firstly, we
give a basic grounding in the fundamentals of SPH, showing how the equations of
motion and energy can be self-consistently derived from the density estimate.
We then show how to interpret these equations using the basic SPH interpolation
formulae and highlight the subtle difference in approach between SPH and other
particle methods. In doing so, we also critique several `urban myths' regarding
SPH, in particular the idea that one can simply increase the `neighbour number'
more slowly than the total number of particles in order to obtain convergence.
We also discuss the origin of numerical instabilities such as the pairing and
tensile instabilities. Finally, we give practical advice on how to resolve
three of the main issues with SPMHD: removing the tensile instability,
formulating dissipative terms for MHD shocks and enforcing the divergence
constraint on the particles, and we give the current status of developments in
this area. Accompanying the paper is the first public release of the NDSPMHD
SPH code, a 1, 2 and 3 dimensional code designed as a testbed for SPH/SPMHD
algorithms that can be used to test many of the ideas and used to run all of
the numerical examples contained in the paper.Comment: 44 pages, 14 figures, accepted to special edition of J. Comp. Phys.
on "Computational Plasma Physics". The ndspmhd code is available for download
from http://users.monash.edu.au/~dprice/ndspmhd
Regularized System Identification
This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors’ reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. This is an open access book
Regularization and Bayesian Learning in Dynamical Systems: Past, Present and Future
Regularization and Bayesian methods for system identification have been
repopularized in the recent years, and proved to be competitive w.r.t.
classical parametric approaches. In this paper we shall make an attempt to
illustrate how the use of regularization in system identification has evolved
over the years, starting from the early contributions both in the Automatic
Control as well as Econometrics and Statistics literature. In particular we
shall discuss some fundamental issues such as compound estimation problems and
exchangeability which play and important role in regularization and Bayesian
approaches, as also illustrated in early publications in Statistics. The
historical and foundational issues will be given more emphasis (and space), at
the expense of the more recent developments which are only briefly discussed.
The main reason for such a choice is that, while the recent literature is
readily available, and surveys have already been published on the subject, in
the author's opinion a clear link with past work had not been completely
clarified.Comment: Plenary Presentation at the IFAC SYSID 2015. Submitted to Annual
Reviews in Contro
Boosting the accuracy of SPH techniques: Newtonian and special-relativistic tests
We study the impact of different discretization choices on the accuracy of
SPH and we explore them in a large number of Newtonian and special-relativistic
benchmark tests. As a first improvement, we explore a gradient prescription
that requires the (analytical) inversion of a small matrix. For a regular
particle distribution this improves gradient accuracies by approximately ten
orders of magnitude and the SPH formulations with this gradient outperform the
standard approach in all benchmark tests. Second, we demonstrate that a simple
change of the kernel function can substantially increase the accuracy of an SPH
scheme. While the "standard" cubic spline kernel generally performs poorly, the
best overall performance is found for a high-order Wendland kernel which allows
for only very little velocity noise and enforces a very regular particle
distribution, even in highly dynamical tests. Third, we explore new SPH volume
elements that enhance the treatment of fluid instabilities and, last, but not
least, we design new dissipation triggers. They switch on near shocks and in
regions where the flow --without dissipation-- starts to become noisy. The
resulting new SPH formulation yields excellent results even in challenging
tests where standard techniques fail completely.Comment: accepted for publication in MNRA
- …