Notes on Vector Calculus (Following Apostol, Schey, and Feynman)

Total Page:16

File Type:pdf, Size:1020Kb

Notes on Vector Calculus (Following Apostol, Schey, and Feynman) Notes on Vector Calculus (following Apostol, Schey, and Feynman) Frank A. Benford May, 2007 1. Dot Product, Cross Product, Scalar Triple Product. The standard inner product in ‘8 is the “dot product,” defined as follows.If +,+ß+ßáß+and ,ß,ßáß, a"#8bab"#8 then 8 +,´+,Þ (1.1) " 33 3" The standard norm in ‘8 is defined in terms of the dot product as +´+++##€+€â€+Þ# (1.2) ll È É"# 8 In ‘‘#$ and the norm of a vector is its length. If ??œ", then is said to be a unit ll vector. Some special notation is used in ‘# and ‘‘$#. A point in is sometimes written BßC and a point in ‘$ is sometimes written BßCßD. Alternatively, the symbols BC, , and abab D replace the indices "ß #ßand 3. For example, we might write + œ+ß+ß+ for a vector abBCD in ‘$. The symbols 3, 45, and denote the three standard unit coordinate vectors. Hence BßCßDœB3€C4€D5and +œ+ß+ß+œ+3€+45€+ . ababBCDBCD Suppose that ?B is a unit vector. For any vector , B†?œœB?Bcos))cos llllll where ) is the angle between ? andB. This means that B†?B is the component of in the direction of ?, and B†??B is the orthogonal projection of onto the subspace of scalar ab multiples of ?? (called the subspace of vectors “spanned” by )Þ This is illustrated below. B ) ! ?B†?? ab Vector Calculus. Page 1 The cross product of two vectors +,+ß+ß+ and ,ß,ß, in ‘$ is defined as aBCDbabBCD +,´+,•+,3€+,•+,45€+,•+, aCDDCbaDBBDbabBCCB ââ345 (1.3) ââ œââ+B++CD. ââ ââ,B,,CD ââ It may be shown that +‚, equals the area of the parallelogram determined by +, and ll (i.e., +,sin ) where ) is the angle between + and ,, !Ÿ)1Ÿ‚), and that +, is llll orthogonal to the plane determined by + and ,. More precisely, the direction of +,‚ is determined by the “right-hand” rule as follows: if the right hand is held with the thumb stuck out and with the fingers curled in the direction of rotation of +, into , then the thumb points in the direction of +,‚. In other words, if the index finger of the right hand is pointed forward and shows the direction of +, and if the middle finger is bent to show the direction of ,, and if the thumb is perpendicular to the plane determined by the index and middle finger, then the thumb points in the direction of +‚‚,. Because +, is orthogonal to both +, and , it follows that +†‚+,œ,†‚+,œ!Þ abab Also, it's clear that +‚œ+!+for any vector . Cross products have the following algebraic properties. +‚,œ•‚,+ ab +‚,€-œ+‚,€‚+- ababab 0+‚,œ00+‚,œ‚+, ababab (where 0 is any scalar). It may be shown that +‚,‚-œÐ+†-Ñ,•Ð+†Ñ,- (1.4) ab for any three vectors +, ,-, and . The scalar triple product of any three vectors +, ,-, and is defined as the scalar +‚,-†Þ ab It may be shown that +‚†,- is the volume of the parallelepiped determined by +,, , kkab and -. This suggests (and itmay be shown to be true) thata cyclic permutation of the three vectors does not affect the scalar triple product; that is, +‚,†-,‚-†+-‚†+,.(1.5) ababab The commutativity of the dot product then implies that the dot and cross products in a scalar triple product may be interchanged: +‚,†-+†,-‚Þ (1.6) abab Vector Calculus. Page 2 Finally, it may be shown that the scalar triple product +,- may be written in terms of a determinant as follows: ab ââ+B++CD ââ +,-Þââ,B,,CD (1.7) abââ ââ-B--CD ââ 2.The Gradient. Let H be a subset of ‘‘8. Definition: a scalar field on HH is a mapping from into ; a vector field on HH is a mapping from into ‘8. Suppose that HH be a subset of ‘:8 and that is a differentiable scalar field defined on . For any point < BßBßáßB in H8, the -tuple ab"#8 `:``:: f::<<´grad ´ßßâß (2.1) abab Œ•`B"`B#8`B (where each partial derivative is evaluated at <) is called the gradient of ::. We'll write f or grad : if the point where the partial derivatives are to be evaluated is clear. The collection of vectors f:Ð<ÑH constitutes a vector field over . Example 1. Let :Ð<<Ñ´´<B##€B€â€B#. Then ll È"# 8 " `:`<"B###•#3 œœ#B3B"€B#8€â€Bœ.(2.2) `B33`B#<abˆ‰ It follows that fœ:<<<•" , (2.3) abll a unit vector in the direction of <. As :: is differentiable, the derivative of at <? with respect to any vector exists and is denoted :wÐ<?àÑ. It may be shown that ::wÐ<à?ÑœfÐ<?ц Þ (2.4) If ? is a unit vector, :wÐ<?àÑ is said to be a directional derivative; it's the rate of change of : with respect to distance in the direction of ?. In this case, :wÐ<à?<Ñœf:)ÐÑcos (2.5) ll where ) is the angle between f:Ð<Ñ and ?. That is, f::Ð<цf?< is the component of ab in the direction of ?Þ As cos )is maximal when ):œ!f, it follows that points in the direction at which : increases fastest, and f::ÐÑ< gives the rate of change of in that direction. ll Vector Calculus. Page 3 Suppose, now, that < is a differentiable vector-valued function that maps an interval of real numbers +ß, into H©‘8. For any >−+ß, we write cdcd <Ð>ÑœBÐ>ÑßBÐ>ÑßáßBÐ>ÑÞ ab"#8 The parameter > is commonly interpreted as time. The vector <Ð>Ñ traces out a curveor “path” in ‘8 as > varies over +ß,. The vector of derivatives cd <wÐ>ÑœBwÐ>ÑßBwwÐ>ÑßáßBÐ>Ñ ab"#8 is called the velocity vector and is tangent to the curve at each point. The norm of the velocity vector <wÐ>Ñ measures the speed at which the curve is traversed. The unit tangent ll vector X Ð>Ñ is defined as <wÐ>Ñ X´XÐ>Ñ´Þ (2.6) <wÐ>Ñ ll The arc-length function = is given by > =Ð>Ñœ<wÐ77Ñ. (2.7) (+ll with derivative given by =wÐ>Ñœ<wÐ>Ñ .(2.8) ll Combining eqs. (2.6) and (2.8), we find thatthe unit tangent vector may be interpreted as the rate of change of < with respect to =: <wÐ>Ñ.<<Î.>..B.B X´œœœ"8ßáßÞ (2.9) =wÐ>Ñ.=Î.>.=Œ•.=.= In ‘$, we write <Ð>ÑœBÐ>Ñ3€CÐ>Ñ45€DÐ>Ñ , so .B.C.D Xœ3€45€Þ .=.=.= Now suppose that :: is a differentiable scalar field defined on H. Let 1´‰<ÞThen 1À+ß,Ä‘ and for each >−+ß, cdcd 1Ð>Ñ´:<Ð>ÑÞ cd Under these assumptions, the function 1 is differentiable, and the derivative 1wÐ>Ñ is given by the following chain rule: 8 `: 1wÐ>Ñœf:<<Ð>ц wÐ>ÑœBw Ð>Ñ (2.10) " 3 cd 3œ" `B3 where each partial derivative is evaluated at <Ð>Ñ. The dot product Vector Calculus. Page 4 f: <XÐ>ÑÐ>Ñ cd is called the directional derivative of :: along the curve. Some authors write .Î.= for this directional derivative, as 8 `::.B . f: X 3 Þ " 3" `B3 .= .= Potential functions. The meaning of “potential function” varies from author to author. Broadly speaking, there are two definitions, one used by mathematicians and the other used by physicists. Mathematicians. Let J be a vector field defined on a set H©‘8. If there exists a scalar field : defined on H such that J f::, then is said to be a potential function for J. Physicists. Let J be a vector field defined on a set H©‘8. If there exists a scalar field Y defined on H such that JJ•fYY, then is said to be a potential function for . In mechanics, the notion of a potential function is applied almost exclusively to force fields. A vector field J is said to be a force field if J< may be interpreted as the force acting ab on a particle at the point <. If there exists a potential function for a force field JJ, then is said to be conservative (for reasons that will be explained later). In other parts of physics, the use of “potential function” is broadened. For example, in electrostatics the force on a charge ; is given by ;ÞII where is a vector field called the “electric field” If there exists a scalar field Y such that Iœ•fYY, then is said to be an electrostatic potential. The two notions of “potential function” differ principally in a sign convention; clearly YÐ<<Ñœ•::ÐÑY. I will attempt to use the symbols and consistently to denote, respectively, the mathematician's and physicist's meaning of “potential function.” Example 2. In ‘$P, let Y<<œ<, where P is an integer and <´ . Generalizing Example 1, it may be showanb that ll •fYœ•P<P•#< Hence Y is a potential function of the force field J<œ•P<P•# . The equipotential surfaces of Y are concentric spheres centered at the origin. Example 3: The Newtonian potential. Newton's law of gravitation says that the force which a particle of mass Q exerts on a particle of mass 7 is a vector of norm K7QÎ<# and directed from the particle of mass 7 towards the particle of mass QK, where is a proportionality constant and < is the distance between the two particles. Hence, if the particle of mass Q7 is placed at the origin and the particle of mass is located at <œB3€C45€D7, then the force acting on the particle of mass is given by Vector Calculus. Page 5 K7Q J•<<where <´Þ <$ ll Using Example 2, we see that J<œ•fY where Yœ•K7Q<•".
Recommended publications
  • Glossary Physics (I-Introduction)
    1 Glossary Physics (I-introduction) - Efficiency: The percent of the work put into a machine that is converted into useful work output; = work done / energy used [-]. = eta In machines: The work output of any machine cannot exceed the work input (<=100%); in an ideal machine, where no energy is transformed into heat: work(input) = work(output), =100%. Energy: The property of a system that enables it to do work. Conservation o. E.: Energy cannot be created or destroyed; it may be transformed from one form into another, but the total amount of energy never changes. Equilibrium: The state of an object when not acted upon by a net force or net torque; an object in equilibrium may be at rest or moving at uniform velocity - not accelerating. Mechanical E.: The state of an object or system of objects for which any impressed forces cancels to zero and no acceleration occurs. Dynamic E.: Object is moving without experiencing acceleration. Static E.: Object is at rest.F Force: The influence that can cause an object to be accelerated or retarded; is always in the direction of the net force, hence a vector quantity; the four elementary forces are: Electromagnetic F.: Is an attraction or repulsion G, gravit. const.6.672E-11[Nm2/kg2] between electric charges: d, distance [m] 2 2 2 2 F = 1/(40) (q1q2/d ) [(CC/m )(Nm /C )] = [N] m,M, mass [kg] Gravitational F.: Is a mutual attraction between all masses: q, charge [As] [C] 2 2 2 2 F = GmM/d [Nm /kg kg 1/m ] = [N] 0, dielectric constant Strong F.: (nuclear force) Acts within the nuclei of atoms: 8.854E-12 [C2/Nm2] [F/m] 2 2 2 2 2 F = 1/(40) (e /d ) [(CC/m )(Nm /C )] = [N] , 3.14 [-] Weak F.: Manifests itself in special reactions among elementary e, 1.60210 E-19 [As] [C] particles, such as the reaction that occur in radioactive decay.
    [Show full text]
  • Lecture 11 Line Integrals of Vector-Valued Functions (Cont'd)
    Lecture 11 Line integrals of vector-valued functions (cont’d) In the previous lecture, we considered the following physical situation: A force, F(x), which is not necessarily constant in space, is acting on a mass m, as the mass moves along a curve C from point P to point Q as shown in the diagram below. z Q P m F(x(t)) x(t) C y x The goal is to compute the total amount of work W done by the force. Clearly the constant force/straight line displacement formula, W = F · d , (1) where F is force and d is the displacement vector, does not apply here. But the fundamental idea, in the “Spirit of Calculus,” is to break up the motion into tiny pieces over which we can use Eq. (1 as an approximation over small pieces of the curve. We then “sum up,” i.e., integrate, over all contributions to obtain W . The total work is the line integral of the vector field F over the curve C: W = F · dx . (2) ZC Here, we summarize the main steps involved in the computation of this line integral: 1. Step 1: Parametrize the curve C We assume that the curve C can be parametrized, i.e., x(t)= g(t) = (x(t),y(t), z(t)), t ∈ [a, b], (3) so that g(a) is point P and g(b) is point Q. From this parametrization we can compute the velocity vector, ′ ′ ′ ′ v(t)= g (t) = (x (t),y (t), z (t)) . (4) 1 2. Step 2: Compute field vector F(g(t)) over curve C F(g(t)) = F(x(t),y(t), z(t)) (5) = (F1(x(t),y(t), z(t), F2(x(t),y(t), z(t), F3(x(t),y(t), z(t))) , t ∈ [a, b] .
    [Show full text]
  • Notes on Partial Differential Equations John K. Hunter
    Notes on Partial Differential Equations John K. Hunter Department of Mathematics, University of California at Davis Contents Chapter 1. Preliminaries 1 1.1. Euclidean space 1 1.2. Spaces of continuous functions 1 1.3. H¨olderspaces 2 1.4. Lp spaces 3 1.5. Compactness 6 1.6. Averages 7 1.7. Convolutions 7 1.8. Derivatives and multi-index notation 8 1.9. Mollifiers 10 1.10. Boundaries of open sets 12 1.11. Change of variables 16 1.12. Divergence theorem 16 Chapter 2. Laplace's equation 19 2.1. Mean value theorem 20 2.2. Derivative estimates and analyticity 23 2.3. Maximum principle 26 2.4. Harnack's inequality 31 2.5. Green's identities 32 2.6. Fundamental solution 33 2.7. The Newtonian potential 34 2.8. Singular integral operators 43 Chapter 3. Sobolev spaces 47 3.1. Weak derivatives 47 3.2. Examples 47 3.3. Distributions 50 3.4. Properties of weak derivatives 53 3.5. Sobolev spaces 56 3.6. Approximation of Sobolev functions 57 3.7. Sobolev embedding: p < n 57 3.8. Sobolev embedding: p > n 66 3.9. Boundary values of Sobolev functions 69 3.10. Compactness results 71 3.11. Sobolev functions on Ω ⊂ Rn 73 3.A. Lipschitz functions 75 3.B. Absolutely continuous functions 76 3.C. Functions of bounded variation 78 3.D. Borel measures on R 80 v vi CONTENTS 3.E. Radon measures on R 82 3.F. Lebesgue-Stieltjes measures 83 3.G. Integration 84 3.H. Summary 86 Chapter 4.
    [Show full text]
  • MULTIVARIABLE CALCULUS (MATH 212 ) COMMON TOPIC LIST (Approved by the Occidental College Math Department on August 28, 2009)
    MULTIVARIABLE CALCULUS (MATH 212 ) COMMON TOPIC LIST (Approved by the Occidental College Math Department on August 28, 2009) Required Topics • Multivariable functions • 3-D space o Distance o Equations of Spheres o Graphing Planes Spheres Miscellaneous function graphs Sections and level curves (contour diagrams) Level surfaces • Planes o Equations of planes, in different forms o Equation of plane from three points o Linear approximation and tangent planes • Partial Derivatives o Compute using the definition of partial derivative o Compute using differentiation rules o Interpret o Approximate, given contour diagram, table of values, or sketch of sections o Signs from real-world description o Compute a gradient vector • Vectors o Arithmetic on vectors, pictorially and by components o Dot product compute using components v v v v v ⋅ w = v w cosθ v v v v u ⋅ v = 0⇔ u ⊥ v (for nonzero vectors) o Cross product Direction and length compute using components and determinant • Directional derivatives o estimate from contour diagram o compute using the lim definition t→0 v v o v compute using fu = ∇f ⋅ u ( u a unit vector) • Gradient v o v geometry (3 facts, and understand how they follow from fu = ∇f ⋅ u ) Gradient points in direction of fastest increase Length of gradient is the directional derivative in that direction Gradient is perpendicular to level set o given contour diagram, draw a gradient vector • Chain rules • Higher-order partials o compute o mixed partials are equal (under certain conditions) • Optimization o Locate and
    [Show full text]
  • Mathematical Methods of Classical Mechanics-Arnold V.I..Djvu
    V.I. Arnold Mathematical Methods of Classical Mechanics Second Edition Translated by K. Vogtmann and A. Weinstein With 269 Illustrations Springer-Verlag New York Berlin Heidelberg London Paris Tokyo Hong Kong Barcelona Budapest V. I. Arnold K. Vogtmann A. Weinstein Department of Department of Department of Mathematics Mathematics Mathematics Steklov Mathematical Cornell University University of California Institute Ithaca, NY 14853 at Berkeley Russian Academy of U.S.A. Berkeley, CA 94720 Sciences U.S.A. Moscow 117966 GSP-1 Russia Editorial Board J. H. Ewing F. W. Gehring P.R. Halmos Department of Department of Department of Mathematics Mathematics Mathematics Indiana University University of Michigan Santa Clara University Bloomington, IN 47405 Ann Arbor, MI 48109 Santa Clara, CA 95053 U.S.A. U.S.A. U.S.A. Mathematics Subject Classifications (1991): 70HXX, 70005, 58-XX Library of Congress Cataloging-in-Publication Data Amol 'd, V.I. (Vladimir Igorevich), 1937- [Matematicheskie melody klassicheskoi mekhaniki. English] Mathematical methods of classical mechanics I V.I. Amol 'd; translated by K. Vogtmann and A. Weinstein.-2nd ed. p. cm.-(Graduate texts in mathematics ; 60) Translation of: Mathematicheskie metody klassicheskoi mekhaniki. Bibliography: p. Includes index. ISBN 0-387-96890-3 I. Mechanics, Analytic. I. Title. II. Series. QA805.A6813 1989 531 '.01 '515-dcl9 88-39823 Title of the Russian Original Edition: M atematicheskie metody klassicheskoi mekhaniki. Nauka, Moscow, 1974. Printed on acid-free paper © 1978, 1989 by Springer-Verlag New York Inc. All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer-Verlag, 175 Fifth Avenue, New York, NY 10010, U.S.A.), except for brief excerpts in connection with reviews or scholarly analysis.
    [Show full text]
  • 1 Curl and Divergence
    Sections 15.5-15.8: Divergence, Curl, Surface Integrals, Stokes' and Divergence Theorems Reeve Garrett 1 Curl and Divergence Definition 1.1 Let F = hf; g; hi be a differentiable vector field defined on a region D of R3. Then, the divergence of F on D is @ @ @ div F := r · F = ; ; · hf; g; hi = f + g + h ; @x @y @z x y z @ @ @ where r = h @x ; @y ; @z i is the del operator. If div F = 0, we say that F is source free. Note that these definitions (divergence and source free) completely agrees with their 2D analogues in 15.4. Theorem 1.2 Suppose that F is a radial vector field, i.e. if r = hx; y; zi, then for some real number p, r hx;y;zi 3−p F = jrjp = (x2+y2+z2)p=2 , then div F = jrjp . Theorem 1.3 Let F = hf; g; hi be a differentiable vector field defined on a region D of R3. Then, the curl of F on D is curl F := r × F = hhy − gz; fz − hx; gx − fyi: If curl F = 0, then we say F is irrotational. Note that gx − fy is the 2D curl as defined in section 15.4. Therefore, if we fix a point (a; b; c), [gx − fy](a; b; c), measures the rotation of F at the point (a; b; c) in the plane z = c. Similarly, [hy − gz](a; b; c) measures the rotation of F in the plane x = a at (a; b; c), and [fz − hx](a; b; c) measures the rotation of F in the plane y = b at the point (a; b; c).
    [Show full text]
  • Vector Calculus and Multiple Integrals Rob Fender, HT 2018
    Vector Calculus and Multiple Integrals Rob Fender, HT 2018 COURSE SYNOPSIS, RECOMMENDED BOOKS Course syllabus (on which exams are based): Double integrals and their evaluation by repeated integration in Cartesian, plane polar and other specified coordinate systems. Jacobians. Line, surface and volume integrals, evaluation by change of variables (Cartesian, plane polar, spherical polar coordinates and cylindrical coordinates only unless the transformation to be used is specified). Integrals around closed curves and exact differentials. Scalar and vector fields. The operations of grad, div and curl and understanding and use of identities involving these. The statements of the theorems of Gauss and Stokes with simple applications. Conservative fields. Recommended Books: Mathematical Methods for Physics and Engineering (Riley, Hobson and Bence) This book is lazily referred to as “Riley” throughout these notes (sorry, Drs H and B) You will all have this book, and it covers all of the maths of this course. However it is rather terse at times and you will benefit from looking at one or both of these: Introduction to Electrodynamics (Griffiths) You will buy this next year if you haven’t already, and the chapter on vector calculus is very clear Div grad curl and all that (Schey) A nice discussion of the subject, although topics are ordered differently to most courses NB: the latest version of this book uses the opposite convention to polar coordinates to this course (and indeed most of physics), but older versions can often be found in libraries 1 Week One A review of vectors, rotation of coordinate systems, vector vs scalar fields, integrals in more than one variable, first steps in vector differentiation, the Frenet-Serret coordinate system Lecture 1 Vectors A vector has direction and magnitude and is written in these notes in bold e.g.
    [Show full text]
  • 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.
    [Show full text]
  • Surface Regularized Geometry Estimation from a Single Image
    SURGE: Surface Regularized Geometry Estimation from a Single Image Peng Wang1 Xiaohui Shen2 Bryan Russell2 Scott Cohen2 Brian Price2 Alan Yuille3 1University of California, Los Angeles 2Adobe Research 3Johns Hopkins University Abstract This paper introduces an approach to regularize 2.5D surface normal and depth predictions at each pixel given a single input image. The approach infers and reasons about the underlying 3D planar surfaces depicted in the image to snap predicted normals and depths to inferred planar surfaces, all while maintaining fine detail within objects. Our approach comprises two components: (i) a four- stream convolutional neural network (CNN) where depths, surface normals, and likelihoods of planar region and planar boundary are predicted at each pixel, followed by (ii) a dense conditional random field (DCRF) that integrates the four predictions such that the normals and depths are compatible with each other and regularized by the planar region and planar boundary information. The DCRF is formulated such that gradients can be passed to the surface normal and depth CNNs via backpropagation. In addition, we propose new planar-wise metrics to evaluate geometry consistency within planar surfaces, which are more tightly related to dependent 3D editing applications. We show that our regularization yields a 30% relative improvement in planar consistency on the NYU v2 dataset [24]. 1 Introduction Recent efforts to estimate the 2.5D layout of a depicted scene from a single image, such as per-pixel depths and surface normals, have yielded high-quality outputs respecting both the global scene layout and fine object detail [2, 6, 7, 29]. Upon closer inspection, however, the predicted depths and normals may fail to be consistent with the underlying surface geometry.
    [Show full text]
  • 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.
    [Show full text]
  • Complex Analysis in Industrial Inverse Problems
    Complex Analysis in Industrial Inverse Problems Bill Lionheart, University of Manchester October 30th , 2019. Isaac Newton Institute How does complex analysis arise in IP? We see complex analysis arise in two main ways: 1. Inverse Problems that are or can be reduced to two dimensional problems. Here we solve inverse problems for PDEs and integral equations using methods from complex analysis where the complex variable represents a spatial variable in the plane. This includes various kinds of tomographic methods involving weighted line integrals, and is used in medial imaging and non-destructive testing. In electromagnetic problems governed by some form of Maxwell’s equation complex analysis is typically used for a plane approximation to a two dimensional problem. 2. Frequency domain methods in which the complex variable is the Fourier-Laplace transform variable. The Hilbert transform is ubiquitous in signal processing (everyone has one implemented in their home!) due to the analytic representation of a signal. In inverse spectral problems analyticity with respect to a spectral parameter plays an important role. Industrial Electromagnetic Inverse Problems Many inverse problems involve imaging the inside from measuring on the outside. Here are some industrial applied inverse problems in electromagnetics I Ground penetrating radar, used for civil engineering eg finding burried pipes and cables. Similar also to microwave imaging (security, medicine) and radar. I Electrical resistivity/polarizability tomography. Used to locate underground pollution plumes, salt water ingress, buried structures, minerals. Also used for industrial process monitoring (pipes, mixing vessels etc). I Metal detecting and inductive imaging. Used to locate weapons on people, find land mines and unexploded ordnance, food safety, scrap metal sorting, locating reinforcing bars in concrete, non-destructive testing, archaeology, etc.
    [Show full text]
  • CS 468 (Spring 2013) — Discrete Differential Geometry 1 the Unit Normal Vector of a Surface. 2 Surface Area
    CS 468 (Spring 2013) | Discrete Differential Geometry Lecture 7 Student Notes: The Second Fundamental Form Lecturer: Adrian Butscher; Scribe: Soohark Chung 1 The unit normal vector of a surface. Figure 1: Normal vector of level set. • The normal line is a geometric feature. The normal direction is not (think of a non-orientable surfaces such as a mobius strip). If possible, you want to pick normal directions that are consistent globally. For example, for a manifold, you can pick normal directions pointing out. • Locally, you can find a normal direction using tangent vectors (though you can't extend this globally). • Normal vector of a parametrized surface: E1 × E2 If TpS = spanfE1;E2g then N := kE1 × E2k This is just the cross product of two tangent vectors normalized by it's length. Because you take the cross product of two vectors, it is orthogonal to the tangent plane. You can see that your choice of tangent vectors and their order determines the direction of the normal vector. • Normal vector of a level set: > [DFp] N := ? TpS kDFpk This is because the gradient at any point is perpendicular to the level set. This makes sense intuitively if you remember that the gradient is the "direction of the greatest increase" and that the value of the level set function stays constant along the surface. Of course, you also have to normalize it to be unit length. 2 Surface Area. • We want to be able to take the integral of the surface. One approach to the problem may be to inegrate a parametrized surface in the parameter domain.
    [Show full text]