Robustifying Learnability

In recent years, the learnability of rational expectations equilibria (REE) and determinacy of economic structures have rightfully joined the usual performance criteria among the sought after goals of policy design. And while some contributions to the literature (for example Bullard and Mitra (2001) and Evans and Honkapohja (2002)) have made significant headway in establishing certain features of monetary policy rules that facilitate learning, a comprehensive treatment of policy design for learnability has yet to surface, especially for cases in which agents have potentially misspecified their learning models. This paper provides such a treatment. We argue that since even among professional economists a generally acceptable workhorse model of the economy has not been agreed upon, it is unreasonable to expect private agents to have collective rational expectations. We assume instead that agents have an approximate understanding of the workings of the economy and that their task of learning true reduced forms of the economy is subject to potentially destabilizing errors. We then ask: can a central bank set policy that accounts for learning errors but also succeeds in bounding them in a way that allows eventual learnability of the model, given policy. For different parameterizations of a given policy rule applied to a New Keynesian model, we use structured singular value analysis (from robust control) to find the largest ranges of misspecifications that can be tolerated in a learning model without compromising convergence to an REE. A parallel set of experiments seeks to determine the optimal stance (strong inflation as opposed to strong output stabilization) that allows for the greatest scope of errors in learning without leading to expectational instabilty in cases when the central bank designs both optimal and robust policy rules with commitment. We compare the features of all the rules contemplated in the paper with those that maximize economic performance in the true model, and we measure the performance cost of maximizing learnability under the various conditions mentioned here.monetary policy, learning, E-stability, model uncertainty, robustness

Robustness analysis for power systems based on the structured singular value tools and the [nu] gap metric

Modern power systems are operated more stressed than ever because of the advent of deregulation and competition. One of the important issues in the design of controllers for a stressed system is to evaluate the stability of the controlled system over a range of operating conditions.;The conventional controllers are designed to make the system stable under certain conditions of operation. The time consuming time domain simulation is then used to evaluate the controllers for a few selected operating conditions around which the controllers are designed. Such a design and evaluation procedure cannot guarantee robustness of the controller over the whole range of operating conditions.;In this dissertation, practical algorithms to perform robustness analysis based on two tools, structured singular value and the nu gap metric, are investigated. The power system stabilizer is used as the controller and small signal stability is of interest.;The robustness problem in the SSV framework is set up for the multimachine power system. In this formulation, an improved uncertainty characterization has been used to capture the effect of parameter variations in terms of the varying elements of the linearized system matries, which are derived from the component differential equations and the network algebraic equations separately. SVD decomposition is used to reduce the size of the problem. Based on the resulting framework, a branch and bound scheme is proposed to intelligently select frequency intervals on which the frequency sweep test can be performed further to find the peak of mu. Instead of blindly choosing frequency intervals to sweep, which could ignore important frequency points on the mu plots, this scheme provides searching under guidance. The analysis procedure accurately predicts the range of stable operating conditions which are verified by repeated eigenvalue analysis.;Fir the robustness in terms of nu gap metric, we set up the feedback configuration for multimachine power system. The frequency response of the nu gap metric is plotted and its relationship with that of the stability margin is used to determine the stability of the perturbed systems. A weighted nu gap metric is defined and its frequency domain interpretation is explored to further reduce the conservatism of the results.;Finally, a feedback configuration is carefully developed to carry out the McFarlane and Glover Hinfinity loop shaping design procedure. The effect of the damping controller on improving system dynamic performance is also examined.;Comparisons are made between the two major analysis tools via the results on the same test systems with the same scenarios

Synchronization in complex networks

Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.Comment: Final version published in Physics Reports. More information available at http://synchronets.googlepages.com

Data Driven Techniques for Modeling Coupled Dynamics in Transient Processes

We study the problem of modeling coupled dynamics in transient processes that happen in a network. The problem is considered at two levels. At the node level, the coupling between underlying sub-processes of a node in a network is considered. At the network level, the direct influence among the nodes is considered. After the model is constructed, we develop a network-based approach for change detection in high dimension transient processes. The overall contribution of our work is a more accurate model to describe the underlying transient dynamics either for each individual node or for the whole network and a new statistic for change detection in multi-dimensional time series. Specifically, at the node level, we developed a model to represent the coupled dynamics between the two processes. We provide closed form formulas on the conditions for the existence of periodic trajectory and the stability of solutions. Numerical studies suggest that our model can capture the nonlinear characteristics of empirical data while reducing computation time by about 25% on average, compared to a benchmark modeling approach. In the last two problems, we provide a closed form formula for the bound in the sparse regression formulation, which helps to reduce the effort of trial and error to find an appropriate bound. Compared to other benchmark methods in inferring network structure from time series, our method reduces inference error by up to 5 orders of magnitudes and maintain better sparsity. We also develop a new method to infer dynamic network structure from a single time series. This method is the basis for introducing a new spectral graph statistic for change detection. This statistic can detect changes in simulation scenario with modified area under curve (mAUC) of 0.96. When applying to the problem of detecting seizure from EEG signal, our statistic can capture the physiology of the process while maintaining a detection rate of 40% by itself. Therefore, it can serve as an effective feature to detect change and can be added to the current set of features for detecting seizures from EEG signal

Recent Advances in Robust Control

Robust control has been a topic of active research in the last three decades culminating in H_2/H_\infty and \mu design methods followed by research on parametric robustness, initially motivated by Kharitonov's theorem, the extension to non-linear time delay systems, and other more recent methods. The two volumes of Recent Advances in Robust Control give a selective overview of recent theoretical developments and present selected application examples. The volumes comprise 39 contributions covering various theoretical aspects as well as different application areas. The first volume covers selected problems in the theory of robust control and its application to robotic and electromechanical systems. The second volume is dedicated to special topics in robust control and problem specific solutions. Recent Advances in Robust Control will be a valuable reference for those interested in the recent theoretical advances and for researchers working in the broad field of robotics and mechatronics

Feedback stabilisation of pool-boiling systems : for application in thermal management schemes

The research scope of this thesis is the stabilisation of unstable states in a pool-boiling system. Thereto, a compact mathematical model is employed. Pool-boiling systems serve as physical model for practical applications of boiling heat transfer in industry. Boiling has advantages over conventional heat-transfer methods based on air- or single-phase liquids by enabling extremely high heat-transfer rates at isothermal conditions. This o¿ers solutions to thermal issues emerging in cutting-edge technologies as semi-conductor manufacturing and electric vehicles (EVs). Continuous miniaturisation in micro-electronics is pushing heat-¿ux densities beyond the limits of standard cooling schemes and growing architecture complexity makes thermal uniformity during chip manufacturing increasingly critical. Further development of EVs may bene¿t equally from boiling heat transfer by its utilisation for actuator cooling and thermal conditioning of battery packs. A pool-boiling system consists of a heater that is submerged in a pool of boiling liquid. The theater is the to-be-cooled device (or a thermally conducting element between the device and the boiling liquid) and is heated at its bottom. On top of the heater, heat is extracted by the boiling liquid. In order to exploit boiling to its fullest e¿ciency, unstable modes need to be stabilised to avoid the formation of a thermally-insulating vapour ¿lm on the heater that causes collapse of the cooling capacity and that heralds a dangerous and ine¿cient mode of boiling. The pool-boiling model comprises a partial di¿erential equation (PDE), i.e. the well- known heat equation, and corresponding boundary conditions that represent adiabatic sidewalls, a uniform heat supply at the bottom, and a nonuniform and nonlinear heat extraction at the heater top. This nonlinear boundary condition renders the entire model nonlinear, resulting in multiple equilibria and complex and exciting dynamics. Restriction to uniform temperature distributions within the heater admits description by a model of one spatial dimension (1D). The 1D model is investigated mathematically and the results are compared with those found by the analyses of spatial-discretisations of the model. Two spatial-discretisation schemes, based on a ¿nite-di¿erence method and a spectral method, are investigated. The latter shows far better convergence properties than the former. Moreover, application of full state feedback of the spectral modes (modal control) results in signi¿cantly better properties than by regulation via standard P-control. In practical applications, the heater temperature can only be measured at the heater top. Consequently, an observer is implemented that estimates the spectral modes of the temperature within the heater, which are subsequently used in the feedback-law. The e¿ciency and performance of this controller-observer combination is examined by numerical simulations. A pool-boiling system with an electrically heated wire as heater can be represented by the model as described above, but now with two spatial dimensions (2D). The 2D model can be analysed mathematically only for uniform equilibria, i.e. the equilibria that exist also for the 1D system. For nonuniform equilibria, the mathematical analysis becomes too complex and a spatial discretisation is required to obtain results. A 1D characteristic equation on the ¿uid-heater interface can be obtained by analytical reduction of the 2D eigenvalue problem using the method of separation of variables. The system poles follow from spatially discretising this equation. Because of its outstanding performance for the 1D model, the 2D model is again stabilised by a modal controller (full state feedback) in combination with an observer. Simulations are again performed to determine the e¿ciency of the controller-observer combination. If a thermally conducting foil is considered as heater, the three-dimensional (3D) form of the model must be investigated. This involves essentially the same methodology as described above, resulting in a 2D characteristic equation on the ¿uid-heater interface. However, spatial discretisation of this equation yields large system matrices and requires excessive calculation times. Hence, the 3D system is analysed only at moderate discretisation orders. The above modal control strategy is, as before, applied in combination with an observer to stabilise unstable equilibria and the evolution of the nonlinear system is again investigated and demonstrated by way of simulations. Finally, a series of exploratory experiments, to investigate the application of pool-boiling to thermally condition battery cells in EVs, is considered. Experiments are performed to investigate the ability for thermal homogenisation of the boiling process and the ability to manipulate the boiling process via the pressure in the boiling chamber. Furthermore, the application of pool-boiling to overcome thermal issues in high-end technologies is investigated by numerical simulations

Learning the shape of protein micro-environments with a holographic convolutional neural network

Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from structure remains a major challenge. Here, we introduce Holographic Convolutional Neural Network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein function, including stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function