Chapter 15 ∇ in Other Coordinates
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Cloaking Via Change of Variables for Second Order Quasi-Linear Elliptic Differential Equations Maher Belgacem, Abderrahman Boukricha
Cloaking via change of variables for second order quasi-linear elliptic differential equations Maher Belgacem, Abderrahman Boukricha To cite this version: Maher Belgacem, Abderrahman Boukricha. Cloaking via change of variables for second order quasi- linear elliptic differential equations. 2017. hal-01444772 HAL Id: hal-01444772 https://hal.archives-ouvertes.fr/hal-01444772 Preprint submitted on 24 Jan 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Cloaking via change of variables for second order quasi-linear elliptic differential equations Maher Belgacem, Abderrahman Boukricha University of Tunis El Manar, Faculty of Sciences of Tunis 2092 Tunis, Tunisia. Abstract The present paper introduces and treats the cloaking via change of variables in the framework of quasi-linear elliptic partial differential operators belonging to the class of Leray-Lions (cf. [14]). We show how a regular near-cloak can be obtained using an admissible (nonsingular) change of variables and we prove that the singular change- of variable-based scheme achieve perfect cloaking in any dimension d ≥ 2. We thus generalize previous results (cf. [7], [11]) obtained in the context of electric impedance tomography formulated by a linear differential operator in divergence form. -
Symmetry and Tensors Rotations and Tensors
Symmetry and tensors Rotations and tensors A rotation of a 3-vector is accomplished by an orthogonal transformation. Represented as a matrix, A, we replace each vector, v, by a rotated vector, v0, given by multiplying by A, 0 v = Av In index notation, 0 X vm = Amnvn n Since a rotation must preserve lengths of vectors, we require 02 X 0 0 X 2 v = vmvm = vmvm = v m m Therefore, X X 0 0 vmvm = vmvm m m ! ! X X X = Amnvn Amkvk m n k ! X X = AmnAmk vnvk k;n m Since xn is arbitrary, this is true if and only if X AmnAmk = δnk m t which we can rewrite using the transpose, Amn = Anm, as X t AnmAmk = δnk m In matrix notation, this is t A A = I where I is the identity matrix. This is equivalent to At = A−1. Multi-index objects such as matrices, Mmn, or the Levi-Civita tensor, "ijk, have definite transformation properties under rotations. We call an object a (rotational) tensor if each index transforms in the same way as a vector. An object with no indices, that is, a function, does not transform at all and is called a scalar. 0 A matrix Mmn is a (second rank) tensor if and only if, when we rotate vectors v to v , its new components are given by 0 X Mmn = AmjAnkMjk jk This is what we expect if we imagine Mmn to be built out of vectors as Mmn = umvn, for example. In the same way, we see that the Levi-Civita tensor transforms as 0 X "ijk = AilAjmAkn"lmn lmn 1 Recall that "ijk, because it is totally antisymmetric, is completely determined by only one of its components, say, "123. -
Chapter 8 Change of Variables, Parametrizations, Surface Integrals
Chapter 8 Change of Variables, Parametrizations, Surface Integrals x0. The transformation formula In evaluating any integral, if the integral depends on an auxiliary function of the variables involved, it is often a good idea to change variables and try to simplify the integral. The formula which allows one to pass from the original integral to the new one is called the transformation formula (or change of variables formula). It should be noted that certain conditions need to be met before one can achieve this, and we begin by reviewing the one variable situation. Let D be an open interval, say (a; b); in R , and let ' : D! R be a 1-1 , C1 mapping (function) such that '0 6= 0 on D: Put D¤ = '(D): By the hypothesis on '; it's either increasing or decreasing everywhere on D: In the former case D¤ = ('(a);'(b)); and in the latter case, D¤ = ('(b);'(a)): Now suppose we have to evaluate the integral Zb I = f('(u))'0(u) du; a for a nice function f: Now put x = '(u); so that dx = '0(u) du: This change of variable allows us to express the integral as Z'(b) Z I = f(x) dx = sgn('0) f(x) dx; '(a) D¤ where sgn('0) denotes the sign of '0 on D: We then get the transformation formula Z Z f('(u))j'0(u)j du = f(x) dx D D¤ This generalizes to higher dimensions as follows: Theorem Let D be a bounded open set in Rn;' : D! Rn a C1, 1-1 mapping whose Jacobian determinant det(D') is everywhere non-vanishing on D; D¤ = '(D); and f an integrable function on D¤: Then we have the transformation formula Z Z Z Z ¢ ¢ ¢ f('(u))j det D'(u)j du1::: dun = ¢ ¢ ¢ f(x) dx1::: dxn: D D¤ 1 Of course, when n = 1; det D'(u) is simply '0(u); and we recover the old formula. -
A Brief Tour of Vector Calculus
A BRIEF TOUR OF VECTOR CALCULUS A. HAVENS Contents 0 Prelude ii 1 Directional Derivatives, the Gradient and the Del Operator 1 1.1 Conceptual Review: Directional Derivatives and the Gradient........... 1 1.2 The Gradient as a Vector Field............................ 5 1.3 The Gradient Flow and Critical Points ....................... 10 1.4 The Del Operator and the Gradient in Other Coordinates*............ 17 1.5 Problems........................................ 21 2 Vector Fields in Low Dimensions 26 2 3 2.1 General Vector Fields in Domains of R and R . 26 2.2 Flows and Integral Curves .............................. 31 2.3 Conservative Vector Fields and Potentials...................... 32 2.4 Vector Fields from Frames*.............................. 37 2.5 Divergence, Curl, Jacobians, and the Laplacian................... 41 2.6 Parametrized Surfaces and Coordinate Vector Fields*............... 48 2.7 Tangent Vectors, Normal Vectors, and Orientations*................ 52 2.8 Problems........................................ 58 3 Line Integrals 66 3.1 Defining Scalar Line Integrals............................. 66 3.2 Line Integrals in Vector Fields ............................ 75 3.3 Work in a Force Field................................. 78 3.4 The Fundamental Theorem of Line Integrals .................... 79 3.5 Motion in Conservative Force Fields Conserves Energy .............. 81 3.6 Path Independence and Corollaries of the Fundamental Theorem......... 82 3.7 Green's Theorem.................................... 84 3.8 Problems........................................ 89 4 Surface Integrals, Flux, and Fundamental Theorems 93 4.1 Surface Integrals of Scalar Fields........................... 93 4.2 Flux........................................... 96 4.3 The Gradient, Divergence, and Curl Operators Via Limits* . 103 4.4 The Stokes-Kelvin Theorem..............................108 4.5 The Divergence Theorem ...............................112 4.6 Problems........................................114 List of Figures 117 i 11/14/19 Multivariate Calculus: Vector Calculus Havens 0. -
Introduction to the Modern Calculus of Variations
MA4G6 Lecture Notes Introduction to the Modern Calculus of Variations Filip Rindler Spring Term 2015 Filip Rindler Mathematics Institute University of Warwick Coventry CV4 7AL United Kingdom [email protected] http://www.warwick.ac.uk/filiprindler Copyright ©2015 Filip Rindler. Version 1.1. Preface These lecture notes, written for the MA4G6 Calculus of Variations course at the University of Warwick, intend to give a modern introduction to the Calculus of Variations. I have tried to cover different aspects of the field and to explain how they fit into the “big picture”. This is not an encyclopedic work; many important results are omitted and sometimes I only present a special case of a more general theorem. I have, however, tried to strike a balance between a pure introduction and a text that can be used for later revision of forgotten material. The presentation is based around a few principles: • The presentation is quite “modern” in that I use several techniques which are perhaps not usually found in an introductory text or that have only recently been developed. • For most results, I try to use “reasonable” assumptions, not necessarily minimal ones. • When presented with a choice of how to prove a result, I have usually preferred the (in my opinion) most conceptually clear approach over more “elementary” ones. For example, I use Young measures in many instances, even though this comes at the expense of a higher initial burden of abstract theory. • Wherever possible, I first present an abstract result for general functionals defined on Banach spaces to illustrate the general structure of a certain result. -
7. Transformations of Variables
Virtual Laboratories > 2. Distributions > 1 2 3 4 5 6 7 8 7. Transformations of Variables Basic Theory The Problem As usual, we start with a random experiment with probability measure ℙ on an underlying sample space. Suppose that we have a random variable X for the experiment, taking values in S, and a function r:S→T . Then Y=r(X) is a new random variabl e taking values in T . If the distribution of X is known, how do we fin d the distribution of Y ? This is a very basic and important question, and in a superficial sense, the solution is easy. 1. Show that ℙ(Y∈B) = ℙ ( X∈r −1( B)) for B⊆T. However, frequently the distribution of X is known either through its distribution function F or its density function f , and we would similarly like to find the distribution function or density function of Y . This is a difficult problem in general, because as we will see, even simple transformations of variables with simple distributions can lead to variables with complex distributions. We will solve the problem in various special cases. Transformed Variables with Discrete Distributions 2. Suppose that X has a discrete distribution with probability density function f (and hence S is countable). Show that Y has a discrete distribution with probability density function g given by g(y)=∑ f(x), y∈T x∈r −1( y) 3. Suppose that X has a continuous distribution on a subset S⊆ℝ n with probability density function f , and that T is countable. -
The Laplace Operator in Polar Coordinates in Several Dimensions∗
The Laplace operator in polar coordinates in several dimensions∗ Attila M´at´e Brooklyn College of the City University of New York January 15, 2015 Contents 1 Polar coordinates and the Laplacian 1 1.1 Polar coordinates in n dimensions............................. 1 1.2 Cartesian and polar differential operators . ............. 2 1.3 Calculating the Laplacian in polar coordinates . .............. 2 2 Changes of variables in harmonic functions 4 2.1 Inversionwithrespecttotheunitsphere . ............ 4 1 Polar coordinates and the Laplacian 1.1 Polar coordinates in n dimensions Let n ≥ 2 be an integer, and consider the n-dimensional Euclidean space Rn. The Laplace operator Rn n 2 2 in is Ln =∆= i=1 ∂ /∂xi . We are interested in solutions of the Laplace equation Lnf = 0 n 2 that are spherically symmetric,P i.e., is such that f depends only on r=1 xi . In order to do this, we need to use polar coordinates in n dimensions. These can be definedP as follows: for k with 1 ≤ k ≤ n 2 k 2 define rk = i=1 xi and put for 2 ≤ k ≤ n write P (1) rk−1 = rk sin φk and xk = rk cos φk. rk−1 = rk sin φk and xk = rk cos φk. The polar coordinates of the point (x1,x2,...,xn) will be (rn,φ2,φ3,...,φn). 2 2 2 In case n = 2, we can write y = x1, x = x2. The polar coordinates (r, θ) are defined by r = x + y , (2) x = r cos θ and y = r sin θ, so we can take r2 = r and φ2 = θ. -
Part IA — Vector Calculus
Part IA | Vector Calculus Based on lectures by B. Allanach Notes taken by Dexter Chua Lent 2015 These notes are not endorsed by the lecturers, and I have modified them (often significantly) after lectures. They are nowhere near accurate representations of what was actually lectured, and in particular, all errors are almost surely mine. 3 Curves in R 3 Parameterised curves and arc length, tangents and normals to curves in R , the radius of curvature. [1] 2 3 Integration in R and R Line integrals. Surface and volume integrals: definitions, examples using Cartesian, cylindrical and spherical coordinates; change of variables. [4] Vector operators Directional derivatives. The gradient of a real-valued function: definition; interpretation as normal to level surfaces; examples including the use of cylindrical, spherical *and general orthogonal curvilinear* coordinates. Divergence, curl and r2 in Cartesian coordinates, examples; formulae for these oper- ators (statement only) in cylindrical, spherical *and general orthogonal curvilinear* coordinates. Solenoidal fields, irrotational fields and conservative fields; scalar potentials. Vector derivative identities. [5] Integration theorems Divergence theorem, Green's theorem, Stokes's theorem, Green's second theorem: statements; informal proofs; examples; application to fluid dynamics, and to electro- magnetism including statement of Maxwell's equations. [5] Laplace's equation 2 3 Laplace's equation in R and R : uniqueness theorem and maximum principle. Solution of Poisson's equation by Gauss's method (for spherical and cylindrical symmetry) and as an integral. [4] 3 Cartesian tensors in R Tensor transformation laws, addition, multiplication, contraction, with emphasis on tensors of second rank. Isotropic second and third rank tensors. -
The Jacobian and Change of Variables
The Jacobian and Change of Variables Icon Placement: Section 3.3, p. 204 This set of exercises examines how a particular determinant called the Jacobian may be used to allow us to change variables in double and triple integrals. First, let us review how a change of variables is done in a single integral: this change is usually called a substitution. If we are attempting to integrate f(x) over the interval a ≤ x ≤ b,we can let x = g(u) and substitute u for x, giving us Z b Z d f(x) dx = f(g(u))g0(u) du a c 0 dx where a = g(c)andb = g(d). Notice the presence of the factor g (u)= du . A similar factor will be introduced when we study substitutions in double and triple integrals, and this factor will involve the Jacobian. We will now study changing variables in double and triple integrals. These intergrals are typically studied in third or fourth semester calculus classes. For more information on these integrals, consult your calculus text; e.g., Reference 2, Chapter 13, \Multiple Integrals." We assume that the reader has a passing familiarity with these concepts. To change variables in double integrals, we will need to change points (u; v)topoints(x; y). 2 2 → Ê That is, we will have a transformation T : Ê with T (u; v)=(x; y). Notice that x and y are functions of u and v;thatis,x = x(u; v)andy = y(u; v). This transformation T may or may not be linear. -
Divergence, Gradient and Curl Based on Lecture Notes by James
Divergence, gradient and curl Based on lecture notes by James McKernan One can formally define the gradient of a function 3 rf : R −! R; by the formal rule @f @f @f grad f = rf = ^{ +| ^ + k^ @x @y @z d Just like dx is an operator that can be applied to a function, the del operator is a vector operator given by @ @ @ @ @ @ r = ^{ +| ^ + k^ = ; ; @x @y @z @x @y @z Using the operator del we can define two other operations, this time on vector fields: Blackboard 1. Let A ⊂ R3 be an open subset and let F~ : A −! R3 be a vector field. The divergence of F~ is the scalar function, div F~ : A −! R; which is defined by the rule div F~ (x; y; z) = r · F~ (x; y; z) @f @f @f = ^{ +| ^ + k^ · (F (x; y; z);F (x; y; z);F (x; y; z)) @x @y @z 1 2 3 @F @F @F = 1 + 2 + 3 : @x @y @z The curl of F~ is the vector field 3 curl F~ : A −! R ; which is defined by the rule curl F~ (x; x; z) = r × F~ (x; y; z) ^{ |^ k^ = @ @ @ @x @y @z F1 F2 F3 @F @F @F @F @F @F = 3 − 2 ^{ − 3 − 1 |^+ 2 − 1 k:^ @y @z @x @z @x @y Note that the del operator makes sense for any n, not just n = 3. So we can define the gradient and the divergence in all dimensions. However curl only makes sense when n = 3. Blackboard 2. The vector field F~ : A −! R3 is called rotation free if the curl is zero, curl F~ = ~0, and it is called incompressible if the divergence is zero, div F~ = 0. -
3 Laplace's Equation
3 Laplace’s Equation We now turn to studying Laplace’s equation ∆u = 0 and its inhomogeneous version, Poisson’s equation, ¡∆u = f: We say a function u satisfying Laplace’s equation is a harmonic function. 3.1 The Fundamental Solution Consider Laplace’s equation in Rn, ∆u = 0 x 2 Rn: Clearly, there are a lot of functions u which satisfy this equation. In particular, any constant function is harmonic. In addition, any function of the form u(x) = a1x1 +:::+anxn for constants ai is also a solution. Of course, we can list a number of others. Here, however, we are interested in finding a particular solution of Laplace’s equation which will allow us to solve Poisson’s equation. Given the symmetric nature of Laplace’s equation, we look for a radial solution. That is, we look for a harmonic function u on Rn such that u(x) = v(jxj). In addition, to being a natural choice due to the symmetry of Laplace’s equation, radial solutions are natural to look for because they reduce a PDE to an ODE, which is generally easier to solve. Therefore, we look for a radial solution. If u(x) = v(jxj), then x u = i v0(jxj) jxj 6= 0; xi jxj which implies 1 x2 x2 u = v0(jxj) ¡ i v0(jxj) + i v00(jxj) jxj 6= 0: xixi jxj jxj3 jxj2 Therefore, n ¡ 1 ∆u = v0(jxj) + v00(jxj): jxj Letting r = jxj, we see that u(x) = v(jxj) is a radial solution of Laplace’s equation implies v satisfies n ¡ 1 v0(r) + v00(r) = 0: r Therefore, 1 ¡ n v00 = v0 r v00 1 ¡ n =) = v0 r =) ln v0 = (1 ¡ n) ln r + C C =) v0(r) = ; rn¡1 1 which implies ½ c1 ln r + c2 n = 2 v(r) = c1 (2¡n)rn¡2 + c2 n ¸ 3: From these calculations, we see that for any constants c1; c2, the function ½ c1 ln jxj + c2 n = 2 u(x) ´ c1 (3.1) (2¡n)jxjn¡2 + c2 n ¸ 3: for x 2 Rn, jxj 6= 0 is a solution of Laplace’s equation in Rn ¡ f0g. -
De Vectores Al´Algebra Geométrica
De Vectores al Algebra´ Geom´etrica Sergio Ramos Ram´ırez, Jos´eAlfonso Ju´arezGonz´alez, Volkswagen de M´exico 72700 San Lorenzo Almecatla, Cuautlancingo, Pue., M´exico Garret Sobczyk Universidad de las Am´ericas-Puebla Departamento de F´ısico-Matem´aticas 72820 Puebla, Pue., M´exico February 4, 2018 Abstract El Algebra´ Geom´etricaes la extensi´onnatural del concepto de vector y de la suma de vectores. Despu´esde revisar las propiedades de la suma vec- torial, definimos la multiplicaci´onde vectores de tal manera que respete el famoso Teorema de Pit´agoras. Varias demostraciones sint´eticasde teo- remas en geometr´ıaeuclidiana pueden as´ıser reemplazadas por elegantes demostraciones algebraicas. Aunque en este art´ıculonos limitamos a 2 y 3 dimensiones, el Algebra´ Geom´etricaes aplicable a cualquier dimensi´on, tanto en geometr´ıaseuclidianas como no-euclidianas. 0 Introducci´on La evoluci´ondel concepto de n´umero,el cual es una noci´oncentral en Matem´aticas, tiene una larga y fascinate historia, la cual abarca muchos siglos y ha sido tes- tigo del florecimiento y ca´ıdade muchas civilizaciones [4]. En lo que respecta a la introducci´onde los conceptos de n´umerosnegativos e imaginarios, Gauss hac´ıala siguiente observaci´onen 1831: \... este tipo de avances, sin embargo, siempre se han hecho en primera instancia con pasos t´ımidosy cautelosos". En el presente art´ıculo,presentamos al lector las ideas y m´etodos m´aselementales del Algebra´ Geom´etrica,la cual fue descubierta por William Kingdon Clifford (1845-1879) poco antes de su muerte [1], y constituye la generalizaci´onnatural de los sistemas de n´umerosreales y complejos, a trav´esde la introducci´onde nuevos entes matem´aticosdenominados n´umeros con direcci´on.