Mathematics

In the present paper, we introduce a new family of θ??methods for solving delay differential equations. New methods are developed using a combination of decomposition technique viz. new iterative method proposed by Daftardar Gejji and Jafari and existing implicit numerical methods. Using Butcher tableau, we observed that new methods are non Runge-Kutta methods. Further, convergence of new methods is investigated along with its stability analysis. Applications to variety of problems indicates that the proposed family of methods is more efficient than existing methods.

The textbook Newton's iteration is practically inapplicable on solutions of nonlinear systems with singular Jacobians. By a simple modification, a novel extension of Newton's iteration regains its local quadratic convergence toward nonisolated solutions that are semiregular as properly defined regardless of whether the system is square, underdetermined or overdetermined while Jacobians can be rank-deficient. Furthermore, the iteration serves as a regularization mechanism for computing singular solutions from empirical data. When a system is perturbed, its nonisolated solutions can be altered substantially or even disappear. The iteration still locally converges to a stationary point that approximates a singular solution of the underlying system with an error bound in the same order of the data accuracy. Geometrically, the iteration approximately approaches the nearest point on the solution manifold. The method simplifies the modeling of nonlinear systems by permitting nonisolated solutions and enables a wide range of applications in algebraic computation.

This article describes a method for constructing approximations to periodic solutions of dynamic Lorenz system with classical values of the system parameters. The author obtained a system of nonlinear algebraic equations in general form concerning of the cyclic frequency, constant terms and amplitudes of harmonics that make up harmonic approximations to the desired solutions. The initial approximation for the Newton method is selected, which converges to a solution describing a periodic solution different from the equilibrium position. The results of a computational experiment are presented. The results are verified using high-precision calculations.

We propose a Proper Orthogonal Decomposition (POD)-Galerkin based Reduced Order Model (ROM) for a Leray model. For the implementation of the model, we combine a two-step algorithm called Evolve-Filter (EF) with a computationally efficient finite volume method. The main novelty of the proposed approach relies in applying spatial filtering both for the collection of the snapshots and in the reduced order model, as well as in considering the pressure field at reduced level. In both steps of the EF algorithm, velocity and pressure fields are approximated by using different POD basis and coefficients. For the reconstruction of the pressures fields, we use a pressure Poisson equation approach. We test our ROM on two benchmark problems: 2D and 3D unsteady flow past a cylinder at Reynolds number 0 <= Re <= 100. The accuracy of the reduced order model is assessed against results obtained with the full order model. For the 2D case, a parametric study with respect to the filtering radius is also presented.

This paper concerns the a priori generalization analysis of the Deep Ritz Method (DRM) [W. E and B. Yu, 2017], a popular neural-network-based method for solving high dimensional partial differential equations. We derive the generalization error bounds of two-layer neural networks in the framework of the DRM for solving two prototype elliptic PDEs: Poisson equation and static Schrödinger equation on the d -dimensional unit hypercube. Specifically, we prove that the convergence rates of generalization errors are independent of the dimension d , under the a priori assumption that the exact solutions of the PDEs lie in a suitable low-complexity space called spectral Barron space. Moreover, we give sufficient conditions on the forcing term and the potential function which guarantee that the solutions are spectral Barron functions. We achieve this by developing a new solution theory for the PDEs on the spectral Barron space, which can be viewed as an analog of the classical Sobolev regularity theory for PDEs.

The roots of a monic polynomial expressed in a Chebyshev basis are known to be the eigenvalues of the so-called colleague matrix, which is a Hessenberg matrix that is the sum of a symmetric tridiagonal matrix and a rank-1 matrix. The rootfinding problem is thus reformulated as an eigenproblem, making the computation of the eigenvalues of such matrices a subject of significant practical importance. In this manuscript, we describe an O( n 2 ) explicit structured QR algorithm for colleague matrices and prove that it is componentwise backward stable, in the sense that the backward error in the colleague matrix can be represented as relative perturbations to its components. A recent result of Noferini, Robol, and Vandebril shows that componentwise backward stability implies that the backward error δc in the vector c of Chebyshev expansion coefficients of the polynomial has the bound ?�δc?�≲?�c?�u , where u is machine precision. Thus, the algorithm we describe has both the optimal backward error in the coefficients and the optimal cost O( n 2 ) . We illustrate the performance of the algorithm with several numerical examples.

Partial differential equations on manifolds have been widely studied and plays a crucial role in many subjects. In our previous work, a class of integral equations was introduced to approximate the Poisson problems on manifolds with Dirichlet and Neumann type boundary conditions. In this paper, we restrict our domain into a compact, two dimensional manifold(surface) embedded in high dimensional Euclid space with Dirichlet boundary. Under such special case, a class of more accurate nonlocal models are set up to approximate the Poisson model. One innovation of our model is that, the normal derivative on the boundary is regarded as a variable so that the second order normal derivative can be explicitly expressed by such variable and the curvature of the boundary. Our concentration is on the well-posedness analysis of the weak formulation corresponding to the integral model and the study of convergence to its PDE counterpart. The main result of our work is that, such surface nonlocal model converges to the standard Poisson problem in a rate of O( δ 2 ) in H 1 norm, where δ is the parameter that denotes the range of support for the kernel of the integral operators. Such convergence rate is currently optimal among all integral models according to the literature. Two numerical experiments are included to illustrate our convergence analysis on the other side.

The present paper deals with the interior solid-fluid interaction problem in harmonic regime with randomly perturbed boundaries. Analysis of the shape derivative and shape Hessian of vector- and tensor-valued functions is provided. Moments of the random solutions are approximated by those of the shape derivative and shape Hessian, and the approximations are of third order accuracy in terms of the size of the boundary perturbation. Our theoretical results are supported by an analytical example on a square domain.

The radiation magnetohydrodynamics (RMHD) system couples the ideal magnetohydrodynamics equations with a gray radiation transfer equation. The main challenge is that the radiation travels at the speed of light while the magnetohydrodynamics changes with fluid. The time scales of these two processes can vary dramatically. In order to use mesh sizes and time steps that are independent of the speed of light, asymptotic preserving (AP) schemes in both space and time are desired. In this paper, we develop an AP scheme in both space and time for the RMHD system. Two different scalings are considered, one results in an equilibrium diffusion limit system, while the other results in a non-equilibrium system. The main idea is to decompose the radiative intensity into three parts, each part is treated differently. The performances of the semi-implicit method are presented, for both optically thin and thick regions, as well as for the radiative shock problem. Comparisons with the semi-analytic solution are given to verify the accuracy and asymptotic properties of the method.

We present a new, stable, mixed finite element (FE) method for linear elastostatics of nearly incompressible solids. The method is the automatic variationally stable FE (AVS-FE) method of Calo, Romkes and Valseth, in which we consider a Petrov-Galerkin weak formulation where the stress and displacement variables are in the space H(div)xH1, respectively. This allows us to employ a fully conforming FE discretization for any elastic solid using classical FE subspaces of H(div) and H1. Hence, the resulting FE approximation yields both continuous stresses and displacements. To ensure stability of the method, we employ the philosophy of the discontinuous Petrov-Galerkin (DPG) method of Demkowicz and Gopalakrishnan and use optimal test spaces. Thus, the resulting FE discretization is stable even as the Poisson ratio approaches 0.5, and the system of linear algebraic equations is symmetric and positive definite. Our method also comes with a built-in a posteriori error estimator as well as well as indicators which are used to drive mesh adaptive refinements. We present several numerical verifications of our method including comparisons to existing FE technologies.