Special Issue "Computational Mathematics, Algorithms, and Data Processing"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational Mathematics".

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Prof. Daniele Mortari
Website
Guest Editor
Department of Aerospace Engineering,Texas A&M University - 3141 TAMU College Station, TX 77843-3141, USA
Interests: attitude and position determination systems; satellite constellations design; sensor data processing; algorithms and linear algebra
Special Issues and Collections in MDPI journals
Prof. Dr. Yalchin Efendiev
Website
Guest Editor
Mobil Chair in Computational Sciences, Professor of Mathematics, Director of Institute for Scientific Computation (ISC), Department of Mathematics & ISC, Texas A&M University, College Station, TX 77843, USA
Interests: multiscale; porous media; upscaling; flow; transport; multiscale finite element
Prof. Dr. Boris Hanin
Website
Guest Editor
Department of Mathematics, Texas A&M University, College Station, TX 77843, USA
Interests: theory of deep learning; mathematical physics; spectral theory; microlocal analysis; random polynomials

Special Issue Information

Dear Colleagues,

The Special Issue “Computational Mathematics, Algorithms, and Data Processing” of MDPI invites both original and survey articles that bring together new mathematical tools and numerical methods for computational problems. This issue of MDPI is motivated by the recent profusion and success of large-scale numerical methods in a variety of applied problems and is focused specifically on ideas that are scalable to large-scale problems and have the potential to significantly improve the current state-of-the-art practices. Some possible topics of interest include: Numerical stability, interpolation, approximation, complexity, numerical linear algebra, differential equations (ordinary, partial), optimization, integral equations, systems of nonlinear equations, compression or distillation, and active learning. All submissions must include a discussion of theoretical guarantees or at least justifications for the methods. Articles that explicitly address patterns, symmetries, and equivalences in problems are particularly encouraged.

Prof. Daniele Mortari
Prof. Dr. Yalchin Efendiev
Prof. Dr. Boris Hanin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (9 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle
Learning Algorithms for Coarsening Uncertainty Space and Applications to Multiscale Simulations
Mathematics 2020, 8(5), 720; https://doi.org/10.3390/math8050720 - 04 May 2020
Abstract
In this paper, we investigate and design multiscale simulations for stochastic multiscale PDEs. As for the space, we consider a coarse grid and a known multiscale method, the generalized multiscale finite element method (GMsFEM). In order to obtain a small dimensional representation of [...] Read more.
In this paper, we investigate and design multiscale simulations for stochastic multiscale PDEs. As for the space, we consider a coarse grid and a known multiscale method, the generalized multiscale finite element method (GMsFEM). In order to obtain a small dimensional representation of the solution in each coarse block, the uncertainty space needs to be partitioned (coarsened). This coarsenining collects realizations that provide similar multiscale features as outlined in GMsFEM (or other method of choice). This step is known to be computationally demanding as it requires many local solves and clustering based on them. In this work, we take a different approach and learn coarsening the uncertainty space. Our methods use deep learning techniques in identifying clusters (coarsening) in the uncertainty space. We use convolutional neural networks combined with some techniques in adversary neural networks. We define appropriate loss functions in the proposed neural networks, where the loss function is composed of several parts that includes terms related to clusters and reconstruction of basis functions. We present numerical results for channelized permeability fields in the examples of flows in porous media. Full article
(This article belongs to the Special Issue Computational Mathematics, Algorithms, and Data Processing)
Show Figures

Figure 1

Open AccessArticle
Angular Correlation Using Rogers-Szegő-Chaos
Mathematics 2020, 8(2), 171; https://doi.org/10.3390/math8020171 - 01 Feb 2020
Abstract
Polynomial chaos expresses a probability density function (pdf) as a linear combination of basis polynomials. If the density and basis polynomials are over the same field, any set of basis polynomials can describe the pdf; however, the most logical choice of polynomials is [...] Read more.
Polynomial chaos expresses a probability density function (pdf) as a linear combination of basis polynomials. If the density and basis polynomials are over the same field, any set of basis polynomials can describe the pdf; however, the most logical choice of polynomials is the family that is orthogonal with respect to the pdf. This problem is well-studied over the field of real numbers and has been shown to be valid for the complex unit circle in one dimension. The current framework for circular polynomial chaos is extended to multiple angular dimensions with the inclusion of correlation terms. Uncertainty propagation of heading angle and angular velocity is investigated using polynomial chaos and compared against Monte Carlo simulation. Full article
(This article belongs to the Special Issue Computational Mathematics, Algorithms, and Data Processing)
Show Figures

Figure 1

Open AccessArticle
Bivariate Thiele-Like Rational Interpolation Continued Fractions with Parameters Based on Virtual Points
Mathematics 2020, 8(1), 71; https://doi.org/10.3390/math8010071 - 02 Jan 2020
Cited by 1
Abstract
The interpolation of Thiele-type continued fractions is thought of as the traditional rational interpolation and plays a significant role in numerical analysis and image interpolation. Different to the classical method, a novel type of bivariate Thiele-like rational interpolation continued fractions with parameters is [...] Read more.
The interpolation of Thiele-type continued fractions is thought of as the traditional rational interpolation and plays a significant role in numerical analysis and image interpolation. Different to the classical method, a novel type of bivariate Thiele-like rational interpolation continued fractions with parameters is proposed to efficiently address the interpolation problem. Firstly, the multiplicity of the points is adjusted strategically. Secondly, bivariate Thiele-like rational interpolation continued fractions with parameters is developed. We also discuss the interpolant algorithm, theorem, and dual interpolation of the proposed interpolation method. Many interpolation functions can be gained through adjusting the parameter, which is flexible and convenient. We also demonstrate that the novel interpolation function can deal with the interpolation problems that inverse differences do not exist or that there are unattainable points appearing in classical Thiele-type continued fractions interpolation. Through the selection of proper parameters, the value of the interpolation function can be changed at any point in the interpolant region under unaltered interpolant data. Numerical examples are given to show that the developed methods achieve state-of-the-art performance. Full article
(This article belongs to the Special Issue Computational Mathematics, Algorithms, and Data Processing)
Show Figures

Figure 1

Open AccessArticle
Mixed Generalized Multiscale Finite Element Method for Darcy-Forchheimer Model
Mathematics 2019, 7(12), 1212; https://doi.org/10.3390/math7121212 - 10 Dec 2019
Abstract
In this paper, the solution of the Darcy-Forchheimer model in high contrast heterogeneous media is studied. This problem is solved by a mixed finite element method (MFEM) on a fine grid (the reference solution), where the pressure is approximated by piecewise constant elements; [...] Read more.
In this paper, the solution of the Darcy-Forchheimer model in high contrast heterogeneous media is studied. This problem is solved by a mixed finite element method (MFEM) on a fine grid (the reference solution), where the pressure is approximated by piecewise constant elements; meanwhile, the velocity is discretized by the lowest order Raviart-Thomas elements. The solution on a coarse grid is performed by using the mixed generalized multiscale finite element method (mixed GMsFEM). The nonlinear equation can be solved by the well known Picard iteration. Several numerical experiments are presented in a two-dimensional heterogeneous domain to show the good applicability of the proposed multiscale method. Full article
(This article belongs to the Special Issue Computational Mathematics, Algorithms, and Data Processing)
Show Figures

Figure 1

Open AccessArticle
Scattered Data Interpolation and Approximation with Truncated Exponential Radial Basis Function
Mathematics 2019, 7(11), 1101; https://doi.org/10.3390/math7111101 - 14 Nov 2019
Cited by 3
Abstract
Surface modeling is closely related to interpolation and approximation by using level set methods, radial basis functions methods, and moving least squares methods. Although radial basis functions with global support have a very good approximation effect, this is often accompanied by an ill-conditioned [...] Read more.
Surface modeling is closely related to interpolation and approximation by using level set methods, radial basis functions methods, and moving least squares methods. Although radial basis functions with global support have a very good approximation effect, this is often accompanied by an ill-conditioned algebraic system. The exceedingly large condition number of the discrete matrix makes the numerical calculation time consuming. The paper introduces a truncated exponential function, which is radial on arbitrary n-dimensional space R n and has compact support. The truncated exponential radial function is proven strictly positive definite on R n while internal parameter l satisfies l n 2 + 1 . The error estimates for scattered data interpolation are obtained via the native space approach. To confirm the efficiency of the truncated exponential radial function approximation, the single level interpolation and multilevel interpolation are used for surface modeling, respectively. Full article
(This article belongs to the Special Issue Computational Mathematics, Algorithms, and Data Processing)
Show Figures

Figure 1

Open AccessArticle
Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations
Mathematics 2019, 7(10), 992; https://doi.org/10.3390/math7100992 - 18 Oct 2019
Cited by 5
Abstract
This article concerns the expressive power of depth in neural nets with ReLU activations and a bounded width. We are particularly interested in the following questions: What is the minimal width w min ( d ) so that ReLU nets of width w [...] Read more.
This article concerns the expressive power of depth in neural nets with ReLU activations and a bounded width. We are particularly interested in the following questions: What is the minimal width w min ( d ) so that ReLU nets of width w min ( d ) (and arbitrary depth) can approximate any continuous function on the unit cube [ 0 , 1 ] d arbitrarily well? For ReLU nets near this minimal width, what can one say about the depth necessary to approximate a given function? We obtain an essentially complete answer to these questions for convex functions. Our approach is based on the observation that, due to the convexity of the ReLU activation, ReLU nets are particularly well suited to represent convex functions. In particular, we prove that ReLU nets with width d + 1 can approximate any continuous convex function of d variables arbitrarily well. These results then give quantitative depth estimates for the rate of approximation of any continuous scalar function on the d-dimensional cube [ 0 , 1 ] d by ReLU nets with width d + 3 . Full article
(This article belongs to the Special Issue Computational Mathematics, Algorithms, and Data Processing)
Open AccessArticle
Prediction of Discretization of GMsFEM Using Deep Learning
Mathematics 2019, 7(5), 412; https://doi.org/10.3390/math7050412 - 08 May 2019
Cited by 1
Abstract
In this paper, we propose a deep-learning-based approach to a class of multiscale problems. The generalized multiscale finite element method (GMsFEM) has been proven successful as a model reduction technique of flow problems in heterogeneous and high-contrast porous media. The key ingredients of [...] Read more.
In this paper, we propose a deep-learning-based approach to a class of multiscale problems. The generalized multiscale finite element method (GMsFEM) has been proven successful as a model reduction technique of flow problems in heterogeneous and high-contrast porous media. The key ingredients of GMsFEM include mutlsicale basis functions and coarse-scale parameters, which are obtained from solving local problems in each coarse neighborhood. Given a fixed medium, these quantities are precomputed by solving local problems in an offline stage, and result in a reduced-order model. However, these quantities have to be re-computed in case of varying media (various permeability fields). The objective of our work is to use deep learning techniques to mimic the nonlinear relation between the permeability field and the GMsFEM discretizations, and use neural networks to perform fast computation of GMsFEM ingredients repeatedly for a class of media. We provide numerical experiments to investigate the predictive power of neural networks and the usefulness of the resultant multiscale model in solving channelized porous media flow problems. Full article
(This article belongs to the Special Issue Computational Mathematics, Algorithms, and Data Processing)
Show Figures

Figure 1

Open AccessFeature PaperArticle
The Multivariate Theory of Connections
Mathematics 2019, 7(3), 296; https://doi.org/10.3390/math7030296 - 22 Mar 2019
Cited by 5
Abstract
This paper extends the univariate Theory of Connections, introduced in (Mortari, 2017), to the multivariate case on rectangular domains with detailed attention to the bivariate case. In particular, it generalizes the bivariate Coons surface, introduced by (Coons, 1984), by providing analytical expressions, called [...] Read more.
This paper extends the univariate Theory of Connections, introduced in (Mortari, 2017), to the multivariate case on rectangular domains with detailed attention to the bivariate case. In particular, it generalizes the bivariate Coons surface, introduced by (Coons, 1984), by providing analytical expressions, called constrained expressions, representing all possible surfaces with assigned boundary constraints in terms of functions and arbitrary-order derivatives. In two dimensions, these expressions, which contain a freely chosen function, g ( x , y ) , satisfy all constraints no matter what the g ( x , y ) is. The boundary constraints considered in this article are Dirichlet, Neumann, and any combinations of them. Although the focus of this article is on two-dimensional spaces, the final section introduces the Multivariate Theory of Connections, validated by mathematical proof. This represents the multivariate extension of the Theory of Connections subject to arbitrary-order derivative constraints in rectangular domains. The main task of this paper is to provide an analytical procedure to obtain constrained expressions in any space that can be used to transform constrained problems into unconstrained problems. This theory is proposed mainly to better solve PDE and stochastic differential equations. Full article
(This article belongs to the Special Issue Computational Mathematics, Algorithms, and Data Processing)
Show Figures

Figure 1

Review

Jump to: Research

Open AccessReview
Trigonometrically-Fitted Methods: A Review
Mathematics 2019, 7(12), 1197; https://doi.org/10.3390/math7121197 - 06 Dec 2019
Abstract
Numerical methods for the solution of ordinary differential equations are based on polynomial interpolation. In 1952, Brock and Murray have suggested exponentials for the case that the solution is known to be of exponential type. In 1961, Gautschi came up with the idea [...] Read more.
Numerical methods for the solution of ordinary differential equations are based on polynomial interpolation. In 1952, Brock and Murray have suggested exponentials for the case that the solution is known to be of exponential type. In 1961, Gautschi came up with the idea of using information on the frequency of a solution to modify linear multistep methods by allowing the coefficients to depend on the frequency. Thus the methods integrate exactly appropriate trigonometric polynomials. This was done for both first order systems and second order initial value problems. Gautschi concluded that “the error reduction is not very substantial unless” the frequency estimate is close enough. As a result, no other work was done in this direction until 1984 when Neta and Ford showed that “Nyström’s and Milne-Simpson’s type methods for systems of first order initial value problems are not sensitive to changes in frequency”. This opened the flood gates and since then there have been many papers on the subject. Full article
(This article belongs to the Special Issue Computational Mathematics, Algorithms, and Data Processing)
Back to TopTop