Stochastic macromodeling of nonlinear systems via polynomial chaos expansion and transfer function trajectories

A novel approach is presented to perform stochastic variability analysis of nonlinear systems. The versatility of the method makes it suitable for the analysis of complex nonlinear electronic systems. The proposed technique is a variation-aware extension of the Transfer Function Trajectory method by means of the Polynomial Chaos expansion. The accuracy with respect to the classical Monte Carlo analysis is verified by means of a relevant numerical example showing a simulation speedup of 1777 X

Developing a conceptual model for exploring emergence

Emergence is a fundamental property of complex systems and can be thought of as a new property or behaviour which appears due to non-linear interactions within the system; emergence may be considered to be the 'product' or by-product of the system. For example, within social systems, social capital, the World Wide Web, law and indeed civilization in general may be considered emergent, although all within different time scales. As our world becomes increasingly more interconnected, understanding how emergence arises and how to design for and manage specific types of emergence is ever more important. To date, the concept of emergence has been mainly used as an explanatory framework (as used by Johnson 2001), to inform the logic of action research (Mitleton-Kelly 2004) or as a means of exploring the range of emergent potential of simulation of real complex systems (Axelrod 2003). If we are to improve our ability to manage and control emergence, we need first to directly study the phenomenon of emergence, its causes and consequences across real complex systems

Symmetric complex-valued RBF receiver for multiple-antenna aided wireless systems

A nonlinear beamforming assisted detector is proposed for multiple-antenna-aided wireless systems employing complex-valued quadrature phase shift-keying modulation. By exploiting the inherent symmetry of the optimal Bayesian detection solution, a novel complex-valued symmetric radial basis function (SRBF)-network-based detector is developed, which is capable of approaching the optimal Bayesian performance using channel-impaired training data. In the uplink case, adaptive nonlinear beamforming can be efficiently implemented by estimating the system’s channel matrix based on the least squares channel estimate. Adaptive implementation of nonlinear beamforming in the downlink case by contrast is much more challenging, and we adopt a cluster-variationenhanced clustering algorithm to directly identify the SRBF center vectors required for realizing the optimal Bayesian detector. A simulation example is included to demonstrate the achievable performance improvement by the proposed adaptive nonlinear beamforming solution over the theoretical linear minimum bit error rate beamforming benchmark

Simulation of long-term influence from technical systems on permafrost with various short-scale and hourly operation modes in Arctic region

Technogenic and climatic influences have a significant impact on the degradation of permafrost. Long-term forecasts of such changes during long-time periods have to be taken into account in the oil and gas and construction industries in view to development the Arctic and Subarctic regions. There are considered constantly operating technical systems (for example, oil and gas wells) that affect changes in permafrost, as well as the technical systems that have a short-term impact on permafrost (for example, flare systems for emergency flaring of associated gas). The second type of technical systems is rather complex for simulation, since it is required to reserve both short and long-scales in computations with variable time steps describing the complex technological processes. The main attention is paid to the simulation of long-term influence on the permafrost from the second type of the technical systems. © 2017 Author(s).The work was supported by Russian Foundation for Basic Research 16–01–00401 and program of scientific research UrB RAS 15–16–1–10

Thermalization in a 1D Rydberg gas: validity of the microcanonical ensemble hypothesis

We question the microcanonical hypothesis, often made to account for the thermalization of complex closed quantum systems, on the specific example of a chain of two-level atoms optically driven by a resonant laser beam and strongly interacting via Rydberg-Rydberg dipole-dipole interactions. Along its (necessarily unitary) evolution, this system is indeed expected to thermalize, i.e. observables, such as the number of excitations, stop oscillating and reach equilibrium-like expectation values. The latter are often calculated through assuming the system can be effectively described by a thermal-like microcanonical state. Here, we compare the distribution of excitations in the chain calculated either according to the microcanonical assumption or through direct exact numerical simulation. This allows us to show the limitations of the thermal equilibrium hypothesis and precise its applicability conditions.Comment: v2: Add comparison with Bettelli et al.'s Monte-Carlo simulation (App. A) + typo correctio

Using the Internet to improve university education

Up to this point, university education has largely remained unaffected by the developments of novel approaches to web-based learning. The paper presents a principled approach to the design of problem-oriented, web-based learning at the university level. The principles include providing authentic contexts with multimedia, supporting collaborative knowledge construction, making thinking visible with dynamic visualisation, quick access to content resources via information and communication technologies, and flexible support by tele-tutoring. These principles are used in the MUNICS learning environment, which is designed to support students of computer science to apply their factual knowledge from the lectures to complex real-world problems. For example, students may model the knowledge management in an educational organisation with a graphical simulation tool. Some more general findings from a formative evaluation study with the MUNICS prototype are reported and discussed. For example, the students' ignorance of the additional content resources is discussed in the light of the well-known finding of insufficient use of help systems in software applications

Generalized-ensemble simulations and cluster algorithms

The importance-sampling Monte Carlo algorithm appears to be the universally optimal solution to the problem of sampling the state space of statistical mechanical systems according to the relative importance of configurations for the partition function or thermal averages of interest. While this is true in terms of its simplicity and universal applicability, the resulting approach suffers from the presence of temporal correlations of successive samples naturally implied by the Markov chain underlying the importance-sampling simulation. In many situations, these autocorrelations are moderate and can be easily accounted for by an appropriately adapted analysis of simulation data. They turn out to be a major hurdle, however, in the vicinity of phase transitions or for systems with complex free-energy landscapes. The critical slowing down close to continuous transitions is most efficiently reduced by the application of cluster algorithms, where they are available. For first-order transitions and disordered systems, on the other hand, macroscopic energy barriers need to be overcome to prevent dynamic ergodicity breaking. In this situation, generalized-ensemble techniques such as the multicanonical simulation method can effect impressive speedups, allowing to sample the full free-energy landscape. The Potts model features continuous as well as first-order phase transitions and is thus a prototypic example for studying phase transitions and new algorithmic approaches. I discuss the possibilities of bringing together cluster and generalized-ensemble methods to combine the benefits of both techniques. The resulting algorithm allows for the efficient estimation of the random-cluster partition function encoding the information of all Potts models, even with a non-integer number of states, for all temperatures in a single simulation run per system size.Comment: 15 pages, 6 figures, proceedings of the 2009 Workshop of the Center of Simulational Physics, Athens, G