A Hybrid Computational Method for Partial Differential Equations

A new higher order finite element method for elliptic partial differential equations on a stationary smooth surface $\Gamma$ is introduced and analyzed. We assume that $\Gamma$ is characterized as the zero level of a level set function $\phi$ and only a finite element approximation $\phi_h$ (of degree $k \geq 1$) of $\phi$ is known. For the discretization of the partial differential equation, finite elements (of degree $m\geq 1$) on a piecewise linear approximation of $\Gamma$ are used. The discretization is lifted to $\Gamma_h$, which denotes the zero level of $\phi_h$, using a quasi-orthogonal coordinate system that is constructed by applying a gradient recovery technique to $\phi_h$. A complete discretization error analysis is presented in which the error is split into a geometric error, a quadrature error, and a finite element approximation error. The main result is a $H^1(\Gamma)$-error bound of the form $c(h^m + h^{k+1})$. Results of numerical experiments illustrate the higher order convergence of t...

Some multiscale methods for partial differential equations by J. Xu and A. Zhou Terascale implicit methods for partial differential equations by D. E. Keyes Boundary conditions for fully implicit two-phase flow models by M. Peszynska, E. W. Jenkins, and M. F. Wheeler Phase-field models of solidification by G. B. McFadden Singularity formation in free-surface Stokes flows by Q. Nie, S. Tanveer, T. F. Dupont, and X. Li A note on cache memory methods for multigrid in three dimensions by C. C. Douglas and D. T. Thorne.

Tremendous progress has been achieved in image analysis and processing, and in particular, the use of partial differential equation (PDE) methods in image analysis has proliferated in recent years. However, PDE methods have seen little application to optical image-based structural damage detection for civil systems. This paper applies the Chan-Vese active contour model and its level set representation to detect concrete surface damage, in this case concrete cracks, in optical images. The detected cracks can be further characterized by extracted geometric quantities including their width, length and area of coverage, and associated with engineering design. With regard to crack width extraction, an approximate method is proposed, which relies on solving for a signed distance function. We test these methods by using synthetic images as well as real multi-temporal images from a laboratory experiment. This paper illustrates that using the proposed methods, cracks with complex topographical patterns can be successfully detected with sufficient accuracy.

RETRACTED: Quartic spline collocation methods for elliptic partial differential equations

A fourth-order kernel-free boundary integral method for implicitly defined surfaces in three space dimensions

Domain decomposition and expanded mixed method for parabolic partial differential equations

Finite Element and Finite Difference Methods for Hyperbolic Partial Differential Equations

This comprehensive introduction to global spectral methods for fractional differential equations from leaders of this emerging field is designed to be accessible to graduate students and researchers across math, science, and engineering. The book begins by covering the foundational fractional calculus concepts needed to understand and model anomalous transport phenomena. The book proceeds to introduce a series of new spectral theories and new families of orthogonal and log-orthogonal functions, then presents corresponding spectral and spectral element methods for fractional differential equations. The book also covers the fractional Laplacian in unbounded and bounded domains and major developments in time-integration of fractional models. The book ends by sampling the wide variety of real-world applications of fractional modeling, including: concentration transport in surface/subsurface dynamics; complex rheology and material damage; and fluid turbulence and geostrophic transport.

Introduction. Computer Program Packages. Typical Problems. Classification of Equations. Discrete Methods. Finite Differences and Computational Molecules. Finite Difference Operators. Method of Weighted Residuals. Finite Elements. Method of Lines. Errors. Stability and Convergence. Irregular Boundaries. Choice of Discrete Network. Dimensionless Forms. Parabolic Equations: Introduction. Properties of a Simple Explicit Method. Fourier Stability Method. Implicit Methods. Additional Stability Considerations. Matrix Stability Analysis. Extension of Matrix Stability Analysis. Consistency, Stability, And Convergence. Pure Initial Value Problems. Variable Coefficients. Examples of Equations with Variable Coefficients. General Concepts of Error Reduction. Methods of Lines (MOL) for Parabolic Equations. Weighted Residuals and the Method of Lines. Bubnov-Galerkin Scheme for Parabolic Equations. Finite Elements and Parabolic Equations. Hermite Basis. Finite Elements and Parabolic Equations. General Basis Fucntion. Finite Elements and Parabolic Equations. Special Basis Functions. Explicity Finite Difference Methods for Nonlinear Problems. Further Applications on One Dimentions. Asymmetric Approximations. Elliptic Equations: Introduction. Simple Finite Difference Schemes. Direct Methods. Iterative Methods. Linear Elliptic Equations. Some Poit Iterative Methods. Convergence of Point Iterative Methods. Rates of Convergence. Accelerations. Conjugate Gradient Method. Extensions of SOR. Auliative Examples of Over-Relaxation. Other Point Iterative Methods. Block Iterative Methods. Alternating Direction Methods. Summary of ADI Results. Triangular Elements. Boundary Element Method (BEM). Spectral Methods. Some Nonlinear Examples. Hyperbolic Equations: Introduction. The Quasilinear System. Introductory Examples. Method of Characteristics. Constant States and Simple Waves. Typical Application of Characteristics. Finite Differences for First-Order Equations. Lax-Wendroff Methods and Other Algorithms Dissipation and Dispersion. Explicity Finite Difference Methods. Attenuation. Implicit Methods for Second-Order Equations. Time Quasilinear Examples. Simultaneous First-Order Equations. Explicit Methods. An Implicity Method for First-Order Equations. Hybrid Methods for First-Order Equations. Finite Elements and the Wave Equation. Spectral Methods and Periodic Systems. Gas Dyunamics in One Space Variable. Eulerian Difference Equations. Lagrangian Difference Equations. Hopscotch Methods for Conservation Laws. Explicity-Implicity Schemes for Conservation Laws. Special Topics: Introduction. Singularities. Shocks. Eigenvale Problems. Parabolic Equations in Segeral Space Variables. Additional Comments on Elliptic Equations. Hyperbolic Equations in Higher Dimensions. Mixed Systems. Higher Order Equations in Elasticity and Vibrations. Computational Fluid Mechanics. Stream Function. Vorticity Method for Fluid Mechanics. Primitive Variable Methods for Fluid Mechanics. Vector Potential Methods for Fluid Mechanics. Introduction to Monte Carlo Mehtods. Fast Fourier Transform and Applications. Method of Fractional Steps. Applications of Group Theory in Computation. Computational Ocean Acoustics. Enclosure Methods. Chapter References. Author Index. Subject Index.

The time required to execute real-world scientific computations is a major issue. A single simulation may last hours, days, or even weeks to reach a certain level of accuracy, despite running on large-scale parallel architectures. Strict time limits may often be imposed too -- 60 minutes in the case of the UK Met Office to produce a forecast. In this thesis, it is demonstrated that by raising the level of abstraction, the performance of a class of numerical methods for solving partial differential equations is improvable with minimal user intervention or, in many circumstances, with no user intervention at all. The use of high level languages to express mathematical problems enables domain-specific optimization via compilers. These automated optimizations are proven to be effective in a variety of real-world applications and computational kernels. The focus is on numerical methods based on unstructured meshes, such as the finite element method. The loop nests for unstructured mesh traversal are often irregular (i.e., they perform non-affine memory accesses, such as A[B[i]]), which makes reordering transformations for data locality essentially impossible for low level compilers. Further, the computational kernels are often characterized by complex mathematical expressions, and manual optimization is simply not conceivable. We discuss algorithmic solutions to these problems and present tools for their automation. These tools -- the COFFEE compiler and the SLOPE library -- are currently in use in frameworks for solving partial differential equations.

This paper is devoted to study the abstract functional differential equation (FDE) of the following form$$\dot{u}(t)=Au(t)+\Phi u_t,$$ where $A$ generates a $C$-regularized semigroup, which is the generalization of $C_0$-semigroup and can be applied to deal with many important differential operators that the $C_0$-semigroup can not be used to. We first show that the $C$-well-posedness of a FDE is equivalent to the$\mathscr{C}$-well-posedness of an abstract Cauchy problem in a productBanach space, where the operator $\mathscr{C}$ is related with theoperator $C$ and will be defined in the following text. Then, by making use of a perturbation result of $C$-regularized semigroup, a sufficient condition is provided for the $C$-well-posedness of FDEs. Moreover, anillustrative application to partial differential equation (PDE) with delay is given in the last section.

TREATISES on this subject have been somewhat numerous of late. We recently noticed in these columns an excellent, but fairly elementary work, “On Ordinary and Partial Differential Equations,” by Prof. Woolsey Johnson. The student who wishes to enter on the profitable perusal of the book before us must be well versed in all the ordinary modes of procedure,1 and then he will find that Dr. Craig is well qualified to lead him through the intricate windings of this difficult branch of mathematics. The advanced student will find the author's analyses of use to him whilst reading the various original memoirs here introduced to him, for the first time, in English. Some may remember that Mr. Forsyth, in his classical treatise, omitted the investigations of Fuchs, the recent researches of Hermite and Halphen, contented himself with a slight sketch of Jacobi's method for partial differential equations, and did not at all touch upon the methods of Cauchy, Lie, and Mayer. The consideration of these matters he reserved for a future volume.

The finite-difference method provides us with a powerful tool for solving a broad range of partial differential equations encountered in mathematical physics and engineering. A preliminary exposition was given in chapters 3, 5, and 10 in the context of ordinary differential equations. In this chap-ter, the formulation and implementation of the method is discussed for partial differential equations. Rather than developing and analyzing the algorithms in their general and abstract form, we prefer to illustrate their application with reference to the pervasive convection–diffiusion–reaction equation.

In scientific and engineering calculation, the effective solution of partial differential equations (PDEs) has great significance. This paper presents an innovative method based on the combination of a U-Net neural network with Fourier neural operators, aiming to improve the accuracy and efficiency of solving partial differential equations. U-Net neural networks with a unique encoding–decoder structure and hopping connections can efficiently extract and integrate spatial-domain features and accurately describe the spatial structure of PDEs. The Fourier neural operator combines the advantages of neural networks and Fourier transform to learn the mapping relationship of high-dimensional data by training the linear transformation parameters in the detailed frequency domain of the data. Fourier transform is used to explore and process the frequency-domain features and capture the frequency characteristics of the solution of partial differential equations. By combining the two, this method not only retains spatial details but also makes full use of frequency-domain information, which significantly reduces the dependence of the model on large-scale data and improves the generalization ability of the model. The experimental results show that compared with the traditional method, this method performs well in various complex partial differential equation (PDE) solving tasks and achieves high accuracy.

Most instructors manage two opposing currents in classroom dynamics. The first is a desire to describe the subject in its broadest sense including all the subtleties and nuances. It gives the students a road map to the field, and if they can appreciate it at the time, it provides structure to a course that might otherwise appear to be a loosely connected series of topics. The second is the desire to ground the subject with concrete examples that students can grasp at the finest level. The examples link prerequisite knowledge with one of the new subjects and provide students with a foundation upon which to construct a deeper understanding. Few courses draw together so many disparate topics as numerical methods for partial differential equations (PDEs). When first exposed to finite differences for numerical PDEs, students will readily notice the regular structures in matrices, but few texts address these structures systematically and connect them directly to the analytical properties of the solution. There are many good examples, but it's hard to see the broader landscape. In this Education section article, “Functions of Difference Matrices Are Toeplitz Plus Hankel,” authors Gil Strang and Shev MacNamara fill in some of the big picture by presenting some new results and observations about the structure of difference matrices for approximating solutions to PDEs. A common approach in the analysis of numerical methods for PDEs is to discretize the spatial operator and leave time continuous so that one studies the semidiscrete system. The exact solution to these systems will be functions of the underlying difference matrices, hence the structure of functions of difference matrices is a natural point of interest for students, instructors, and researchers. Toeplitz matrices, having entries that are constant along diagonals, arise naturally in difference matrices. As the authors point out, this is to be expected because of the translation-invariance of the differentiation operator. In an unbounded domain, if initial condition $u_0$ leads to a solution $u$, a translation of $u_0$ will lead to a solution that is a translation of $u$. The authors discover that solutions to the semidiscrete systems are Toeplitz plus Hankel. The role of Hankel matrices, matrices that are constant along antidiagonals, is a little more subtle to explain. If one considers the role of boundary conditions for canonical problems like the heat or wave equations, one can see how this might arise. In the case of a single Neumann boundary condition for the heat equation, the classic method of images provides some insight. Shifting the initial condition toward the boundary shifts the image solution toward the boundary outside the domain, which shifts the influence of the image away from the boundary within the domain. Thus, shifting the initial condition has both a Toeplitz part from transition invariance and a Hankel part from the influence of the boundaries. The Toeplitz plus Hankel decomposition paves the way for exact solutions to semidiscrete approximations, allowing better error estimates and a deeper general understanding of spatial discretization. While it makes sense intuitively that functions of difference matrices should be Toeplitz plus Hankel, the authors enthusiastically explore the topic using a variety of methods and techniques from advanced undergraduate or introductory graduate mathematics including spectral decompositions, Riemann sums, and Fourier analysis. In other words, the destination is interesting, but the voyage is even more fun! This paper would serve as an excellent module for a numerical analysis course or an integrative seminar for undergraduates or graduate students interested in numerical methods.