Finite element analysis of transonic flows in cascades: Importance of computational grids in improving accuracy and convergence

The finite element method is applied for the solution of transonic potential flows through a cascade of airfoils. Convergence characteristics of the solution scheme are discussed. Accuracy of the numerical solutions is investigated for various flow regions in the transonic flow configuration. The design of an efficient finite element computational grid is discussed for improving accuracy and convergence

Experimental and numerical study of SiON microresonators with air and polymer cladding

A systematic experimental and numerical study of the device performance of waveguide-coupled SiON microresonators with air and polymer cladding is presented. Values of device parameters like propagation losses of the microresonator modes, the off-resonance insertion losses, and the straight waveguide to microresonator coupling are determined by applying a detailed fitting procedure to the experimental results and compared to results of detailed numerical simulations. By comparing the propagation losses of the fundamental TE polarized microresonator mode obtained by fitting to the measured spectra to the also experimentally determined propagation losses in the adjacent straight waveguide and the materials losses, it is possible to identify the loss mechanisms in the microresonator. By comparing experimental results for microresonators with air and polymethylmethacrylate cladding and a detailed numerical study, the influence of the cladding index on the bend losses is evaluated. It is demonstrated that the presence of an upper cladding can, under the right conditions, actually be beneficial for loss reduction

Outlier Detection Using Nonconvex Penalized Regression

This paper studies the outlier detection problem from the point of view of penalized regressions. Our regression model adds one mean shift parameter for each of the $n$ data points. We then apply a regularization favoring a sparse vector of mean shift parameters. The usual $L_1$ penalty yields a convex criterion, but we find that it fails to deliver a robust estimator. The $L_1$ penalty corresponds to soft thresholding. We introduce a thresholding (denoted by $\Theta$) based iterative procedure for outlier detection ($\Theta$-IPOD). A version based on hard thresholding correctly identifies outliers on some hard test problems. We find that $\Theta$-IPOD is much faster than iteratively reweighted least squares for large data because each iteration costs at most $O(np)$ (and sometimes much less) avoiding an $O(np^2)$ least squares estimate. We describe the connection between $\Theta$-IPOD and $M$-estimators. Our proposed method has one tuning parameter with which to both identify outliers and estimate regression coefficients. A data-dependent choice can be made based on BIC. The tuned $\Theta$-IPOD shows outstanding performance in identifying outliers in various situations in comparison to other existing approaches. This methodology extends to high-dimensional modeling with $p\gg n$, if both the coefficient vector and the outlier pattern are sparse

Approximating Probability Densities by Iterated Laplace Approximations

The Laplace approximation is an old, but frequently used method to approximate integrals for Bayesian calculations. In this paper we develop an extension of the Laplace approximation, by applying it iteratively to the residual, i.e., the difference between the current approximation and the true function. The final approximation is thus a linear combination of multivariate normal densities, where the coefficients are chosen to achieve a good fit to the target distribution. We illustrate on real and artificial examples that the proposed procedure is a computationally efficient alternative to current approaches for approximation of multivariate probability densities. The R-package iterLap implementing the methods described in this article is available from the CRAN servers.Comment: to appear in Journal of Computational and Graphical Statistics, http://pubs.amstat.org/loi/jcg