Self-adaptive grids for noise mapping refinement

Noise maps are usually represented as contour or isolines maps describing the sound levels in a region. Using this kind of representation the user can easily find the noise level assigned to every location in the map. But the acoustic calculations behind the map are not performed for every single location on it; they are only performed in a grid of receivers. The results in this calculation grid are interpolated to draw the isolines or contours. Therefore, the resolution of the calculation grid and the way it was created (rectangular, triangulated, random…) have an effect on the resulting map. In this paper we describe a smart iterative procedure to optimize the quality of the map at a really low additional computational cost, using self-adaptive grids for the acoustic calculations. These self-adaptive grids add new receivers to the sampling grid in those locations where they are expected to be more useful, so that the performance at the output of the interpolator is enhanced. Self-adaptive sampling grids can be used for minimizing the overall error of the map (improving its quality), or for reducing calculation times, and can be also applied selectively to target areas or contour lines. This can be done by the user customizing the maximum number of iterations, the number of new receivers for each iteration, the target isolines, the target quality…

Aerodynamic optimization of the ICE2 high-speed train nose using a genetic algorithm and metamodels

An aerodynamic optimization of the ICE 2 high-speed train nose in term of front wind action sensitivity is carried out in this paper. The nose is parametrically defined by Be?zier Curves, and a three-dimensional representation of the nose is obtained using thirty one design variables. This implies a more complete parametrization, allowing the representation of a real model. In order to perform this study a genetic algorithm (GA) is used. Using a GA involves a large number of evaluations before finding such optimal. Hence it is proposed the use of metamodels or surrogate models to replace Navier-Stokes solver and speed up the optimization process. Adaptive sampling is considered to optimize surrogate model fitting and minimize computational cost when dealing with a very large number of design parameters. The paper introduces the feasi- bility of using GA in combination with metamodels for real high-speed train geometry optimization.

Independent doubly Adaptive Rejection Metropolis Sampling

Adaptive Rejection Metropolis Sampling (ARMS) is a wellknown MCMC scheme for generating samples from onedimensional target distributions. ARMS is widely used within Gibbs sampling, where automatic and fast samplers are often needed to draw from univariate full-conditional densities. In this work, we propose an alternative adaptive algorithm (IA2RMS) that overcomes the main drawback of ARMS (an uncomplete adaptation of the proposal in some cases), speeding up the convergence of the chain to the target. Numerical results show that IA2RMS outperforms the standard ARMS, providing a correlation among samples close to zero.

An adaptive population importance sampler

Monte Carlo (MC) methods are widely used in signal processing, machine learning and communications for statistical inference and stochastic optimization. A well-known class of MC methods is composed of importance sampling and its adaptive extensions (e.g., population Monte Carlo). In this work, we introduce an adaptive importance sampler using a population of proposal densities. The novel algorithm provides a global estimation of the variables of interest iteratively, using all the samples generated. The cloud of proposals is adapted by learning from a subset of previously generated samples, in such a way that local features of the target density can be better taken into account compared to single global adaptation procedures. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error and robustness to initialization.