Expected optimal feedback with Time-Varying Parameters

In this paper we derive the closed loop form of the Expected Optimal Feedback rule, sometimes called passive learning stochastic control, with time varying parameters. As such this paper extends the work of Kendrick (1981,2002, Chapter 6) where parameters are assumed to vary randomly around a known constant mean. Furthermore, we show that the cautionary myopic rule in Beck and Wieland (2002) model, a test bed for comparing various stochastic optimizations approaches, can be cast into this framework and can be treated as a special case of this solution.Optimal experimentation, stochastic optimization, time-varying parameters, expected optimal feedback

Expected optimal feedback with Time-Varying Parameters

In this paper we derive, by using dynamic programming, the closed loop form of the Expected Optimal Feedback rule with time varying parameter. As such this paper extends the work of Kendrick (1981, 2002, Chapter 6) for the time varying parameter case. Furthermore, we show that the Beck and Wieland (2002) model can be cast into this framework and can be treated as a special case of this solution.

Entanglement of 2xK quantum systems

We derive an analytical expression for the lower bound of the concurrence of mixed quantum states of composite 2xK systems. In contrast to other, implicitly defined entanglement measures, the numerical evaluation of our bound is straightforward. We explicitly evaluate its tightness for general mixed states of 2x3 systems, and identify a large class of states where our expression gives the exact value of the concurrence.Comment: 7 pages, 1 figure, to be published in Europhysics Lette

The CIB-lensing bispectrum: impact on primordial non-Gaussianity and detectability for the Planck mission

We characterize the cosmic infrared background (CIB)-lensing bispectrum which is one of the contributions to the three-point functions of cosmic microwave background (CMB) maps in harmonic space. We show that the CIB-lensing bispectrum has a considerable strength and that it can be detected with high significance in the Planck high-frequency maps. We also present forecasts of the contamination on different shapes of the primordial non-Gaussianity fnl parameter produced by the CIB-lensing bispectrum and by the extragalactic point sources bispectrum in the Planck high-resolution CMB anisotropy maps. The local, equilateral and orthogonal shapes are considered for ‘raw' single-frequency (i.e. without applying any component separation technique) and foreground-reduced Planck temperature maps. The CIB-lensing correlation seems to mainly affect orthogonal shapes of the bispectrum - with $\Delta f_{\rm nl}^{\rm (ort)} =-21$ and −88 for the 143 and 217 GHz bands, respectively - while point sources mostly impact equilateral shapes, with $\Delta f_{\rm nl}^{\rm (eq)} =160, 54$ and 60 at 100, 143 and 217 GHz. However, the results indicate that these contaminants do not induce any relevant bias on Planck fnl estimates when foreground-reduced maps are considered: using SEVEM for the component separation, we obtain $\Delta f_{\rm nl}^{\rm (ort)} =10.5$ due to the CIB-lensing and $\Delta f_{\rm nl}^{\rm (eq)}=30.4$ due to point sources, corresponding to 0.3σ and 0.45σ in terms of the Planck 2013 fnl uncertainty. The component separation technique is, in fact, able to partially clean the extragalactic source contamination and the bias is reduced for all the shapes. We have further developed single- and multiple-frequency estimators based on the Komatsu, Spergel & Wandelt formalism that can be implemented to efficiently detect this signa

Down-Hole Heat Exchangers: Modelling of a Low-Enthalpy Geothermal System for District Heating

In order to face the growing energy demands, renewable energy sources can provide an alternative to fossil fuels. Thus, low-enthalpy geothermal plants may play a fundamental role in those areas—such as the Province of Viterbo—where shallow groundwater basins occur and conventional geothermal plants cannot be developed. This may lead to being fuelled by locally available sources. The aim of the present paper is to exploit the heat coming from a low-enthalpy geothermal system. The experimental plant consists in a down-hole heat exchanger for civil purposes and can supply thermal needs by district heating. An implementation in MATLAB environment is provided in order to develop a mathematical model. As a consequence, the amount of withdrawable heat can be successfully calculated

A regularized procedure to generate a deep learning model for topology optimization of electromagnetic devices

The use of behavioral models based on deep learning (DL) to accelerate electromagnetic field computations has recently been proposed to solve complex electromagnetic problems. Such problems usually require time-consuming numerical analysis, while DL allows achieving the topo-logically optimized design of electromagnetic devices using desktop class computers and reasonable computation times. An unparametrized bitmap representation of the geometries to be optimized, which is a highly desirable feature needed to discover completely new solutions, is perfectly managed by DL models. On the other hand, optimization algorithms do not easily cope with high dimensional input data, particularly because it is difficult to enforce the searched solutions as feasible and make them belong to expected manifolds. In this work, we propose the use of a variational autoencoder as a data regularization/augmentation tool in the context of topology optimization. The optimization was carried out using a gradient descent algorithm, and the DL neural network was used as a surrogate model to accelerate the resolution of single trial cases in the due course of optimization. The varia-tional autoencoder and the surrogate model were simultaneously trained in a multi-model custom training loop that minimizes total loss—which is the combination of the two models’ losses. In this paper, using the TEAM 25 problem (a benchmark problem for the assessment of electromagnetic numerical field analysis) as a test bench, we will provide a comparison between the computational times and design quality for a “classical” approach and the DL-based approach. Preliminary results show that the variational autoencoder manages regularizing the resolution process and transforms a constrained optimization into an unconstrained one, improving both the quality of the final solution and the performance of the resolution process