Abstract: In this contribution, we are concerned with tight a posteriori error estimation for projection-based model order reduction of \(\inf \)-\(\sup \) stable parameterized variational problems. In particular, we consider the Reduced Basis Method in a Petrov-Galerkin framework, where the reduced approximation spaces are constructed by the (weak) greedy algorithm. We propose and analyze a hierarchical a posteriori error estimator which evaluates the difference of two reduced approximations of different accuracy. Based on the a priori error analysis of the (weak) greedy algorithm, it is expected that the hierarchical error estimator is sharp with efficiency index close to one, if the Kolmogorov N-with decays fast for the underlying problem and if a suitable saturation assumption for the reduced approximation is satisfied. We investigate the tightness of the hierarchical a posteriori estimator both from a theoretical and numerical perspective. For the respective approximation with higher accuracy, we study and compare basis enrichment of Lagrange- and Taylor-type reduced bases. Numerical experiments indicate the efficiency for both, the construction of a reduced basis using the hierarchical error estimator in a greedy algorithm, and for tight online certification of reduced approximations. This is particularly relevant in cases where the \(\inf \)-\(\sup \) constant may become small depending on the parameter. In such cases, a standard residual-based error estimator—complemented by the successive constrained method to compute a lower bound of the parameter dependent \(\inf \)-\(\sup \) constant—may become infeasible. PubDate: 2019-11-14

Abstract: We consider model order reduction for a free boundary problem of an osmotic cell that is parameterized by material parameters as well as the initial shape of the cell. Our approach is based on an Arbitrary-Lagrangian-Eulerian description of the model that is discretized by a mass-conservative finite element scheme. Using reduced basis techniques and empirical interpolation, we construct a parameterized reduced order model in which the mass conservation property of the full-order model is exactly preserved. Numerical experiments are provided that highlight the performance of the resulting reduced order model. PubDate: 2019-11-13

Abstract: The integral version of the fractional Laplacian on a bounded domain is discretized by a Galerkin approximation based on piecewise linear functions on a quasiuniform mesh. We show that the inverse of the associated stiffness matrix can be approximated by blockwise low-rank matrices at an exponential rate in the block rank. PubDate: 2019-11-13

Abstract: Markov chain Monte Carlo (MCMC) sampling of posterior distributions arising in Bayesian inverse problems is challenging when evaluations of the forward model are computationally expensive. Replacing the forward model with a low-cost, low-fidelity model often significantly reduces computational cost; however, employing a low-fidelity model alone means that the stationary distribution of the MCMC chain is the posterior distribution corresponding to the low-fidelity model, rather than the original posterior distribution corresponding to the high-fidelity model. We propose a multifidelity approach that combines, rather than replaces, the high-fidelity model with a low-fidelity model. First, the low-fidelity model is used to construct a transport map that deterministically couples a reference Gaussian distribution with an approximation of the low-fidelity posterior. Then, the high-fidelity posterior distribution is explored using a non-Gaussian proposal distribution derived from the transport map. This multifidelity “preconditioned” MCMC approach seeks efficient sampling via a proposal that is explicitly tailored to the posterior at hand and that is constructed efficiently with the low-fidelity model. By relying on the low-fidelity model only to construct the proposal distribution, our approach guarantees that the stationary distribution of the MCMC chain is the high-fidelity posterior. In our numerical examples, our multifidelity approach achieves significant speedups compared with single-fidelity MCMC sampling methods. PubDate: 2019-11-13

Abstract: This paper focuses on the model reduction problem for a special class of linear parameter-varying systems. This kind of systems can be reformulated as bilinear dynamical systems. Based on the bilinear system theory, we give a definition of the \(\mathcal {H}_{2}\) norm in the generalized frequency domain. Then, a model reduction method is proposed based on the gradient descent on the Grassmann manifold. The merit of the method is that by utilizing the gradient flow analysis, the algorithm is guaranteed to converge, and further speedup of the convergence rate can be achieved as well. Two numerical examples are tested to demonstrate the proposed method. PubDate: 2019-11-13

Abstract: In this paper, we shall consider an adapted finite difference preconditioning technique for radial basis function (RBF) method. This technique, for the system of equations that arises from solving Helmholtz equation with Dirichlet boundary conditions by RBF collocation method, is considered. We prove that the eigenvalues of preconditioned matrices are bounded for one and two-dimensional cases. We also show that RBF interpolating matrix can be well-conditioned by using appropriate number of polynomial bases in augmented term of RBF approximation. Numerical experiments show the efficiency and robustness of the preconditioning procedure, particularly when there are non-zero boundary conditions. PubDate: 2019-11-13

Abstract: The velocity correction method has shown to be an effective approach for solving incompressible Navier–Stokes equations. It does not require the initial pressure and the inf-sup condition may not be needed. However, stability and convergence analyses have not been established for the nonlinear case. The challenge arises from the splitting associated with the nonlinear term and rotational term. In this paper, we carry out stability and convergence analysis of the first-order method in the nonlinear case. Our technique is a new Gauge–Uzawa formulation, which brings forth a telescoping symmetry into the rotational form. We also provide a stability proof for the second-order method in the linear case. Numerical results are provided for both first- and second-order methods. PubDate: 2019-11-12

Abstract: A statistical learning approach for high-dimensional parametric PDEs related to uncertainty quantification is derived. The method is based on the minimization of an empirical risk on a selected model class, and it is shown to be applicable to a broad range of problems. A general unified convergence analysis is derived, which takes into account the approximation and the statistical errors. By this, a combination of theoretical results from numerical analysis and statistics is obtained. Numerical experiments illustrate the performance of the method with the model class of hierarchical tensors. PubDate: 2019-10-05

Abstract: We consider model order reduction based on proper orthogonal decomposition (POD) for unsteady incompressible Navier-Stokes problems, assuming that the snapshots are given by spatially adapted finite element solutions. We propose two approaches of deriving stable POD-Galerkin reduced-order models for this context. In the first approach, the pressure term and the continuity equation are eliminated by imposing a weak incompressibility constraint with respect to a pressure reference space. In the second approach, we derive an inf-sup stable velocity-pressure reduced-order model by enriching the velocity-reduced space with supremizers computed on a velocity reference space. For problems with inhomogeneous Dirichlet conditions, we show how suitable lifting functions can be obtained from standard adaptive finite element computations. We provide a numerical comparison of the considered methods for a regularized lid-driven cavity problem. PubDate: 2019-09-11

Abstract: We present an offline/online computational procedure for computing the dual norm of parameterized linear functionals. The approach is motivated by the need to efficiently compute residual dual norms, which are used in model reduction to estimate the error of a given reduced solution. The key elements of the approach are (i) an empirical test space for the manifold of Riesz elements associated with the parameterized functional and (ii) an empirical quadrature procedure to efficiently deal with parametrically non-affine terms. We present a number of theoretical and numerical results to identify the different sources of error and to motivate the proposed technique, and we compare the approach with other state-of-the-art techniques. Finally, we investigate the effectiveness of our approach to reduce both offline and online costs associated with the computation of the time-averaged residual indicator proposed in Fick et al. (J. Comput. Phys. 371, 214–243 2018). PubDate: 2019-09-11

Abstract: A direct boundary integral equation method for the heat equation based on Nyström discretization is proposed and analyzed. For problems with moving geometries, a weakly and strongly singular Green’s integral equation is formulated. Here the hypersingular integral operator, i.e., the normal trace of the double-layer potential, must be understood as a Hadamard finite part integral. The thermal layer potentials are regarded as generalized Abel integral operators in time and discretized with a singularity-corrected trapezoidal rule. The spatial discretization is a standard quadrature rule for smooth surface integrals. The discretized systems lead to an explicit time stepping scheme and is effective for solving the Dirichlet and Neumann boundary value problems based on both the weakly and/or strongly singular integral equations. PubDate: 2019-08-27

Abstract: In this article, we consider the problems (unsolved in the literature) of computing the nearest normal matrix X to a given non-normal matrix A, under certain constraints, that are (i) if A is real, we impose that also X is real; (ii) if A has known entries on a given sparsity pattern Ω and unknown/uncertain entries otherwise, we impose to X the constraint xij = aij for all entries (i,j) in the pattern Ω. As far as we know, there do not exist in the literature specific algorithms aiming to solve these problems. For the case in which all entries of A can be modified, there exists an algorithm by Ruhe, which is able to compute the closest normal matrix. However, if A is real, the closest computed matrix by Ruhe’s algorithm might be complex, which motivates the development of a different algorithm preserving reality. Normality is characterized in a very large number of ways; in this article, we consider the property that the square of the Frobenius norm of a normal matrix is equal to the sum of the squares of the moduli of its eigenvalues. This characterization allows us to formulate as equivalent problem the minimization of a functional of an unknown matrix, which should be normal, fulfill the required constraints, and have minimal distance from the given matrix A. PubDate: 2019-08-27

Abstract: It is known that wave equations have physically very important properties which should be respected by numerical schemes in order to predict correctly the solution over a long time period. In this paper, the long-time behaviour of momentum and actions for energy-preserving methods is analysed for semi-linear wave equations. A full discretisation of wave equations is derived and analysed by firstly using a spectral semi-discretisation in space and then by applying the adopted average vector field (AAVF) method in time. This numerical scheme can exactly preserve the energy of the semi-discrete system. The main theme of this paper is to analyse another important physical property of the scheme. It is shown that this scheme yields near conservation of a modified momentum and modified actions over long times. The results are rigorously proved based on the technique of modulated Fourier expansions in two stages. First, a multi-frequency modulated Fourier expansion of the AAVF method is constructed, and then two almost-invariants of the modulation system are derived. PubDate: 2019-08-16

Abstract: The image of a polynomial map is a constructible set. While computing its closure is standard in computer algebra systems, a procedure for computing the constructible set itself is not. We provide a new algorithm, based on algebro-geometric techniques, addressing this problem. We also apply these methods to answer a question of W. Hackbusch on the non-closedness of site-independent cyclic matrix product states for infinitely many parameters. PubDate: 2019-08-08

Abstract: The following paper describes a numerical simulation of a complete bypass of a stenosed human artery. The considered geometry consists of the narrowed host tube and the bypass graft with a 45-degree angle of connection. Different diameters of the narrowing are tested. Blood is the fluid with shear rate–dependent viscosity; therefore, various rheology mathematical models for generalized Newtonian fluids are considered, namely Cross model, modified Cross model, Carreau model, and Carreau-Yasuda model. The fundamental system of equations is based on the system of generalized Navier-Stokes equations. Generalized Newtonian fluids flow in a bypass tube is numerically simulated by using a SIMPLE algorithm included in the open-source CFD tool, OpenFOAM. The aim of this work is to compare the numerical results for the different mathematical models of the viscosity with the changing diameter of the narrowed channel. PubDate: 2019-08-01

Abstract: This paper discusses a discrete-time queueing system in which an arriving customer may adopt four different strategies; two of them correspond to a LCFS discipline where displacements or expulsions occur, and in the other two, the arriving customer decides to follow a FCFS discipline or to become a negative customer eliminating the customer in the server, if any. The different choices of the involved parameters make this model to enjoy a great versatility, having several special cases of interest. We carry out a thorough analysis of the system, and using a generating function approach, we derive analytical results for the stationary distributions obtaining performance measures for the number of customers in the queue and in the system. Also, recursive formulae for calculating the steady-state distributions of the queue and system size has been developed. Making use of the busy period of an auxiliary system, the sojourn times of a customer in the queue and in the system have also been obtained. Finally, some numerical examples are given. PubDate: 2019-08-01

Abstract: Continuous inkjet systems are commonly used to print expiry date labels for food products. These systems are designed to print on flat surfaces; however, a lot of food products package have a cylindrical shape (e.g., bottled and canned products) which causes an enlargement in characters at the ends of the label. In this work, we present an algorithm to correct this defect by calculating the extra-distance that an ink drop travels when the printing surface approaches an elliptic cylinder. Each charged ink drop is modeling as a solid particle which is affected by the air drag, Earth’s gravitation, and voltage due to the electrical field that causes the perturbation in the ink drop path. Numerical results show the correction of the enlargement mentioned above by varying the electric field along the width of the label. In addition, the equation and the values of a second electric field to correct the printing’s inclination caused by the method of the system’s operation are presented. PubDate: 2019-08-01

Abstract: The “Game of life” model was created in 1970 by the mathematician John Horton Conway using cellular automata. Since then, different extensions of these cellular automata have been used in many applications. In this work, we introduce probabilistic cellular automata which include non-deterministic rules for transitions between successive generations of the automaton together with probabilistic decisions about life and death of the cells in the next generation of the automaton. Different directions of the neighbours of each cell are treated with the possibility of applying distinct probabilities. This way, more realistic situations can be modelled and the obtained results are also non-deterministic. In this paper, we include a brief state of the art, the description of the model and some examples obtained with an implementation of the model made in Java. PubDate: 2019-08-01

Abstract: This paper focuses on the generalization ability of a dendritic neuron model (a model of a simple neural network). The considered model is an extension of the Hodgkin-Huxley model. The Markov kinetic schemes have been used in the mathematical description of the model, while the Lagrange multipliers method has been applied to train the model. The generalization ability of the model is studied using a method known from the regularization theory, in which a regularizer is added to the neural network error function. The regularizers in the form of the sum of squared weights of the model (the penalty function), a linear differential operator related to the input-output mapping (the Tikhonov functional), and the square norm of the network curvature are applied in the study. The influence of the regularizers on the training process and its results are illustrated with the problem of noise reduction in images of electronic components. Several metrics are used to compare results obtained for different regularizers. PubDate: 2019-08-01

Abstract: The article deals with the numerical simulation of unsteady flows through the turbine part of the turbocharger. The main focus of the article is the extension of the in-house CFD finite volume solver for the case of unsteady flows in radial turbines and the coupling to an external zero-dimensional model of the inlet and outlet parts. In the second part, brief description of a simplified one-dimensional model of the turbine is given. The final part presents a comparison of the results of numerical simulations using both the 3D CFD method and the 1D simplified model with the experimental data. The comparison shows that the properly calibrated 1D model gives accurate predictions of mass flow rate and turbine performance at much less computational time than the full 3D CFD method. On the other hand, the more expensive 3D CFD method does not need any specific calibration and allows detailed inspections of the flow fields. PubDate: 2019-08-01