Steady-state response of a random dynamical system described with Padé approximants and random eigenmodes

Designing a random dynamical system requires the prediction of the statistics of the response, knowing the random model of the uncertain parameters. Direct Monte Carlo simulation (MCS) is the reference method for propagating uncertainties but its main drawback is the high numerical cost. A surrogate model based on a polynomial chaos expansion (PCE) can be built as an alternative to MCS. However, some previous studies have shown poor convergence properties around the deterministic eigenfrequencies. In this study, an extended Pade approximant approach is proposed not only to accelerate the convergence of the PCE but also to have a better representation of the exact frequency response, which is a rational function of the uncertain parameters. A second approach is based on the random mode expansion of the response, which is widely used for deterministic dynamical systems. A PCE approach is used to calculate the random modes. Both approaches are tested on an example to check their efficiency

Dynamical Systems in Spiking Neuromorphic Hardware

Dynamical systems are universal computers. They can perceive stimuli, remember, learn from feedback, plan sequences of actions, and coordinate complex behavioural responses. The Neural Engineering Framework (NEF) provides a general recipe to formulate models of such systems as coupled sets of nonlinear differential equations and compile them onto recurrently connected spiking neural networks – akin to a programming language for spiking models of computation. The Nengo software ecosystem supports the NEF and compiles such models onto neuromorphic hardware. In this thesis, we analyze the theory driving the success of the NEF, and expose several core principles underpinning its correctness, scalability, completeness, robustness, and extensibility. We also derive novel theoretical extensions to the framework that enable it to far more effectively leverage a wide variety of dynamics in digital hardware, and to exploit the device-level physics in analog hardware. At the same time, we propose a novel set of spiking algorithms that recruit an optimal nonlinear encoding of time, which we call the Delay Network (DN). Backpropagation across stacked layers of DNs dramatically outperforms stacked Long Short-Term Memory (LSTM) networks—a state-of-the-art deep recurrent architecture—in accuracy and training time, on a continuous-time memory task, and a chaotic time-series prediction benchmark. The basic component of this network is shown to function on state-of-the-art spiking neuromorphic hardware including Braindrop and Loihi. This implementation approaches the energy-efficiency of the human brain in the former case, and the precision of conventional computation in the latter case

Polynomial chaos-based extended Padé expansion in structural dynamics

The response of a random dynamical system is totally characterized by its probability density function (pdf). However, determining a pdf by a direct approach requires a high numerical cost; similarly, surrogate models such as direct polynomial chaos expansions are not generally efficient, especially around the eigenfrequencies of the dynamical system. In the present study, a new approach based on Padé approximants to obtain moments and pdf of the dynamic response in the frequency domain is proposed. A key difference between the direct polynomial chaos representation and the Padé representation is that the Padé approach has polynomials in both numerator and denominator. For frequency response functions, the denominator plays a vital role as it contains the information related to resonance frequencies, which are uncertain. A Galerkin approach in conjunction with polynomial chaos is proposed for the Padé approximation. Another physics-based approach, utilizing polynomial chaos expansions of the random eigenmodes, is proposed and compared with the proposed Padé approach. It is shown that both methods give accurate results even if a very low degree of the polynomial expansion is used. The methods are demonstrated for two degree-of-freedom system with one and two uncertain parameters

An efficient algorithm for accelerating the convergence of oscillatory series, useful for computing the polylogarithm and Hurwitz zeta functions

This paper sketches a technique for improving the rate of convergence of a general oscillatory sequence, and then applies this series acceleration algorithm to the polylogarithm and the Hurwitz zeta function. As such, it may be taken as an extension of the techniques given by Borwein's "An efficient algorithm for computing the Riemann zeta function", to more general series. The algorithm provides a rapid means of evaluating Li_s(z) for general values of complex s and the region of complex z values given by |z^2/(z-1)|<4. Alternatively, the Hurwitz zeta can be very rapidly evaluated by means of an Euler-Maclaurin series. The polylogarithm and the Hurwitz zeta are related, in that two evaluations of the one can be used to obtain a value of the other; thus, either algorithm can be used to evaluate either function. The Euler-Maclaurin series is a clear performance winner for the Hurwitz zeta, while the Borwein algorithm is superior for evaluating the polylogarithm in the kidney-shaped region. Both algorithms are superior to the simple Taylor's series or direct summation. The primary, concrete result of this paper is an algorithm allows the exploration of the Hurwitz zeta in the critical strip, where fast algorithms are otherwise unavailable. A discussion of the monodromy group of the polylogarithm is included.Comment: 37 pages, 6 graphs, 14 full-color phase plots. v3: Added discussion of a fast Hurwitz algorithm; expanded development of the monodromy v4:Correction and clarifiction of monodrom

Series Representations and Approximation of some Quantile Functions appearing in Finance

It has long been agreed by academics that the inversion method is the method of choice for generating random variates, given the availability of a cheap but accurate approximation of the quantile function. However for several probability distributions arising in practice a satisfactory method of approximating these functions is not available. The main focus of this thesis will be to develop Taylor and asymptotic series representations for quantile functions of the following probability distributions; Variance Gamma, Generalized Inverse Gaussian, Hyperbolic, -Stable and Snedecor’s F distributions. As a secondary matter we briefly investigate the problem of approximating the entire quantile function. Indeed with the availability of these new analytic expressions a whole host of possibilities become available. We outline several algorithms and in particular provide a C++ implementation for the variance gamma case. To our knowledge this is the fastest available algorithm of its sort

Multigrid Preconditioners for the Discontinuous Galerkin Spectral Element Method : Construction and Analysis

Discontinuous Galerkin (DG) methods offer a great potential for simulations of turbulent and wall bounded flows with complex geometries since these high-order schemes offer a great potential in handling eddies. Recently, space-time DG methods have become more popular. These discretizations result in implicit schemes of high order in both spatial and temporal directions. In particular, we consider a specific DG variant, the DG Spectral Element Method (DG-SEM), which is suitable to construct entropy stable solvers for conservation laws. Since the size of the corresponding nonlinear systems is dependent on the order of the method in all dimensions, the problem arises to efficiently solve these huge nonlinear systems with regards to CPU time as well as memory consumption.Currently, there is a lack of good solvers for three-dimensional DG applications, which is one of the major obstacles why these high order methods are not used in e.g. industry. We suggest to use Jacobian-free Newton- Krylov (JFNK) solvers, which are advantageous in memory minimization. In order to improve the convergence speed of these solvers, an efficient preconditioner needs to be constructed for the Krylov sub-solver. However, if the preconditioner requires the storage of the DG system Jacobian, the favorable memory consumption of the JFNK approach is obsolete.We therefore present a multigrid based preconditioner for the Krylov sub-method which retains the low mem- ory consumption, i.e. a Jacobian-free preconditioner. To achieve this, we make use of an auxiliary first order finite volume replacement operator. With this idea, the original DG mesh is refined but can still be implemented algebraically. As smoother, we consider the pseudo time iteration W3 with a symmetric Gauss-Seidel type approx- imation of the Jacobian. Numerical results are presented demonstrating the potential of the new approach.In order to analyze multigrid preconditioners, a common tool is the Local Fourier Analysis (LFA). For a space- time model problem we present this analysis and its benefits for calculating smoothing and two-grid convergence factors, which give more insight into the efficiency of the multigrid method

Dynamic soil-structure interaction analysis using the scaled boundary finite-element method.

This thesis presents the development of a reliable and efficient technique for the numerical simulation of dynamic soil-structure interaction problems in anisotropic and nonhomogeneous unbounded soils of arbitrary geometry. Such a technique is indispensable in the seismic analysis of large-scale engineering constructions and, to my best knowledge, does not exist at present. The theoretical framework of the research is based on the scaled boundary finite-element method. The following advances are achieved: The scaled boundary finite-element method is extended to simulate the dynamic response of non-homogeneous unbounded domains. The scaled boundary finite element equations in the frequency and time domains are derived for power-type non-homogeneity frequently employed in geotechnical engineering. A high-frequency asymptotic expansion of the dynamic-stiffness matrix is developed. The frequency domain analysis is performed by integrating the scaled boundary finite-element equation in dynamic stiffness. In the time domain, the scaled boundary finite-element equation including convolution integrals is solved for the unit-impulse response at discrete time stations. A Padé series solution for the scaled boundary finite-element equation in dynamic stiffness is developed. It converges over the whole frequency range as the order of the approximation increases. The computationally expensive task of numerically integrating the scaled boundary finite-element equation is circumvented. Exploiting the sparsity of the coefficientmatrices in the scaled boundary finite-element equation leads to a significant reduction in computer time and memory requirements for solving large-scale problems. Furthermore, lumped coefficient matrices are obtained by adopting the auss-Lobatto-Legendre shape functions with nodal quadrature, which avoids the eigenvalue problem in determining the asymptotic expansion. A high-order local transmitting boundary constructed from a continued-fraction solution of the dynamic-stiffness matrix is developed. An equation of motion as occurring in standard structural dynamics with symmetric and frequency-independent coefficient matrices is obtained. This transmitting boundary condition can be coupled seamlessly with standard finite elements. Transient responses are evaluated by using a standard timeintegration scheme. The expensive task of evaluating convolution integrals is circumvented. The advances developed in this thesis are applicable in other disciplines of engineering and science to the analysis of scalar and vector waves in unbounded media