Mathematical Theorems
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General Relativity Fall 2018 Lecture 10: Integration, Einstein-Hilbert Action
General Relativity Fall 2018 Lecture 10: Integration, Einstein-Hilbert action Yacine Ali-Ha¨ımoud (Dated: October 3, 2018) σ σ HW comment: T µν antisym in µν does NOT imply that Tµν is antisym in µν. Volume element { Consider a LICS with primed coordinates. The 4-volume element is dV = d4x0 = 0 0 0 0 dx0 dx1 dx2 dx3 . If we change coordinates, we have 0 ! @xµ d4x0 = det d4x: (1) µ @x Now, the metric components change as 0 0 0 0 @xµ @xν @xµ @xν g = g 0 0 = η 0 0 ; (2) µν @xµ @xν µ ν @xµ @xν µ ν since the metric components are ηµ0ν0 in the LICS. Seing this as a matrix operation and taking the determinant, we find 0 !2 @xµ det(g ) = − det : (3) µν @xµ Hence, we find that the 4-volume element is q 4 p 4 p 4 dV = − det(gµν )d x ≡ −g d x ≡ jgj d x: (4) The integral of a scalar function f is well defined: given any coordinate system (even if only defined locally): Z Z f dV = f pjgj d4x: (5) We can only define the integral of a scalar function. The integral of a vector or tensor field is mean- ingless in curved spacetime. Think of the integral as a sum. To sum vectors, you need them to belong to the same vector space. There is no common vector space in curved spacetime. Only in flat spacetime can we define such integrals. First parallel-transport the vector field to a single point of spacetime (it doesn't matter which one). -
On the Spectrum of Volume Integral Operators in Acoustic Scattering M Costabel
On the Spectrum of Volume Integral Operators in Acoustic Scattering M Costabel To cite this version: M Costabel. On the Spectrum of Volume Integral Operators in Acoustic Scattering. C. Constanda, A. Kirsch. Integral Methods in Science and Engineering, Birkhäuser, pp.119-127, 2015, 978-3-319- 16726-8. 10.1007/978-3-319-16727-5_11. hal-01098834v2 HAL Id: hal-01098834 https://hal.archives-ouvertes.fr/hal-01098834v2 Submitted on 20 Apr 2015 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. 1 On the Spectrum of Volume Integral Operators in Acoustic Scattering M. Costabel IRMAR, Université de Rennes 1, France; [email protected] 1.1 Volume Integral Equations in Acoustic Scattering Volume integral equations have been used as a theoretical tool in scattering theory for a long time. A classical application is an existence proof for the scattering problem based on the theory of Fredholm integral equations. This approach is described for acoustic and electromagnetic scattering in the books by Colton and Kress [CoKr83, CoKr98] where volume integral equations ap- pear under the name “Lippmann-Schwinger equations”. In electromagnetic scattering by penetrable objects, the volume integral equation (VIE) method has also been used for numerical computations. -
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. -
An Introduction to Fluid Mechanics: Supplemental Web Appendices
An Introduction to Fluid Mechanics: Supplemental Web Appendices Faith A. Morrison Professor of Chemical Engineering Michigan Technological University November 5, 2013 2 c 2013 Faith A. Morrison, all rights reserved. Appendix C Supplemental Mathematics Appendix C.1 Multidimensional Derivatives In section 1.3.1.1 we reviewed the basics of the derivative of single-variable functions. The same concepts may be applied to multivariable functions, leading to the definition of the partial derivative. Consider the multivariable function f(x, y). An example of such a function would be elevations above sea level of a geographic region or the concentration of a chemical on a flat surface. To quantify how this function changes with position, we consider two nearby points, f(x, y) and f(x + ∆x, y + ∆y) (Figure C.1). We will also refer to these two points as f x,y (f evaluated at the point (x, y)) and f . | |x+∆x,y+∆y In a two-dimensional function, the “rate of change” is a more complex concept than in a one-dimensional function. For a one-dimensional function, the rate of change of the function f with respect to the variable x was identified with the change in f divided by the change in x, quantified in the derivative, df/dx (see Figure 1.26). For a two-dimensional function, when speaking of the rate of change, we must also specify the direction in which we are interested. For example, if the function we are considering is elevation and we are standing near the edge of a cliff, the rate of change of the elevation in the direction over the cliff is steep, while the rate of change of the elevation in the opposite direction is much more gradual. -
Lecture 33 Classical Integration Theorems in the Plane
Lecture 33 Classical Integration Theorems in the Plane In this section, we present two very important results on integration over closed curves in the plane, namely, Green’s Theorem and the Divergence Theorem, as a prelude to their important counterparts in R3 involving surface integrals. Green’s Theorem in the Plane (Relevant section from Stewart, Calculus, Early Transcendentals, Sixth Edition: 16.4) 2 1 Let F(x,y) = F1(x,y) i + F2(x,y) j be a vector field in R . Let C be a simple closed (piecewise C ) curve in R2 that encloses a simply connected (i.e., “no holes”) region D ⊂ R. Also assume that the ∂F ∂F partial derivatives 2 and 1 exist at all points (x,y) ∈ D. Then, ∂x ∂y ∂F ∂F F · dr = 2 − 1 dA, (1) IC Z ZD ∂x ∂y where the line integration is performed over C in a counterclockwise direction, with D lying to the left of the path. Note: 1. The integral on the left is a line integral – the circulation of the vector field F over the closed curve C. 2. The integral on the right is a double integral over the region D enclosed by C. Special case: If ∂F ∂F 2 = 1 , (2) ∂x ∂y for all points (x,y) ∈ D, then the integrand in the double integral of Eq. (1) is zero, implying that the circulation integral F · dr is zero. IC We’ve seen this situation before: Recall that the above equality condition for the partial derivatives is the condition for F to be conservative. -
Math 132 Exam 1 Solutions
Math 132, Exam 1, Fall 2017 Solutions You may have a single 3x5 notecard (two-sided) with any notes you like. Otherwise you should have only your pencils/pens and eraser. No calculators are allowed. No consultation with any other person or source of information (physical or electronic) is allowed during the exam. All electronic devices should be turned off. Part I of the exam contains questions 1-14 (multiple choice questions, 5 points each) and true/false questions 15-19 (1 point each). Your answers in Part I should be recorded on the “scantron” card that is provided. If you need to make a change on your answer card, be sure your original answer is completely erased. If your card is damaged, ask one of the proctors for a replacement card. Only the answers marked on the card will be scored. Part II has 3 “written response” questions worth a total of 25 points. Your answers should be written on the pages of the exam booklet. Please write clearly so the grader can read them. When a calculation or explanation is required, your work should include enough detail to make clear how you arrived at your answer. A correct numeric answer with sufficient supporting work may not receive full credit. Part I 1. Suppose that If and , what is ? A) B) C) D) E) F) G) H) I) J) so So and therefore 2. What are all the value(s) of that make the following equation true? A) B) C) D) E) F) G) H) I) J) all values of make the equation true For any , 3. -
Introduction to Theoretical Methods Example
Differentiation of integrals and Leibniz rule • We are interested in solving problems of the type d Z v(x) f (x; t)dt dx u(x) • Notice in addition to the limits depending on x, the function f (x; t) also depends on x • First lets take the case where the limits are constants (u(x) = b and v(x) = a) • In most instances we can take the derivative inside the integral (as a partial derivative) d Z b Z b @f (x; t) f (x; t)dt = dt dx a a @x Patrick K. Schelling Introduction to Theoretical Methods Example R 1 n −kt2 • Find 0 t e dt for n odd • First solve for n = 1, 1 Z 2 I = te−kt dt 0 • Use u = kt2 and du = 2ktdt, so 1 1 Z 2 1 Z 1 I = te−kt dt = e−udu = 0 2k 0 2k dI R 1 3 −kt2 1 • Next notice dk = 0 −t e dt = − 2k2 • We can take successive derivatives with respect to k, so we find Z 1 2n+1 −kt2 n! t e dt = n+1 0 2k Patrick K. Schelling Introduction to Theoretical Methods Another example... the even n case R 1 n −kt2 • The even n case can be done of 0 t e dt • Take for I in this case (writing in terms of x instead of t) 1 Z 2 I = e−kx dx −∞ • We can write for I 2, 1 1 Z Z 2 2 I 2 = e−k(x +y )dxdy −∞ −∞ • Make a change of variables to polar coordinates, x = r cos θ, y = r sin θ, and x2 + y 2 = r 2 • The dxdy in the integrand becomes rdrdθ (we will explore this more carefully in the next chapter) Patrick K. -
Chapter 3: Mathematics of the Poisson Equations
3 Mathematics of the Poisson Equation 3.1 Green functions and the Poisson equation (a) The Dirichlet Green function satisfies the Poisson equation with delta-function charge 2 3 − r GD(r; ro) = δ (r − ro) (3.1) and vanishes on the boundary. It is the potential at r due to a point charge (with unit charge) at ro in the presence of grounded (Φ = 0) boundaries The simplest free space green function is just the point charge solution 1 Go = (3.2) 4πjr − roj In two dimensions the Green function is −1 G = log jr − r j (3.3) o 2π o which is the potential from a line of charge with charge density λ = 1 (b) With Dirichlet boundary conditions the Laplacian operator is self-adjoint. The dirichlet Green function is symmetric GD(r; r0) = GD(r0; r). This is known as the Green Reciprocity Theorem, and appears in many clever ways. The intuitive way to understand this is that for grounded boundary b.c. −∇2 is a real self adjoint 2 3 operator (i.e. a real symmetric matrix). Now −∇ GD(r; r0) = δ (r − r0), so in a functional sense 2 GD(r; r0) is the inverse matrix of −∇ . The inverse of a real symmetric matrix is also real and sym- metric. If the Laplace equation with Dirichlet b.c. is discretized for numerical work, these statements become explicitly rigorous. (c) The Poisson equation or the boundary value problem of the Laplace equation can be solved once the Dirichlet Green function is known. Z Z 3 Φ(r) = d xo GD(r; ro)ρ(ro) − dSo no · rro GD(r; ro)Φ(ro) (3.4) V @V where no is the outward directed normal. -
Applications of the Hodge Decomposition to Biological Structure and Function Modeling
Applications of the Hodge Decomposition to Biological Structure and Function Modeling Andrew Gillette joint work with Chandrajit Bajaj and John Luecke Department of Mathematics, Institute of Computational Engineering and Sciences University of Texas at Austin, Austin, Texas 78712, USA http://www.math.utexas.edu/users/agillette university-logo Computational Visualization Center , I C E S ( DepartmentThe University of Mathematics, of Texas atInstitute Austin of Computational EngineeringDec2008 and Sciences 1/32 University Introduction Molecular dynamics are governed by electrostatic forces of attraction and repulsion. These forces are described as the solutions of a PDE over the molecular surfaces. Molecular surfaces may have complicated topological features affecting the solution. The Hodge Decomposition relates topological properties of the surface to solution spaces of PDEs over the surface. ∼ (space of forms) = (solutions to ∆u = f 6≡ 0) ⊕ (non-trivial deRham classes)university-logo Computational Visualization Center , I C E S ( DepartmentThe University of Mathematics, of Texas atInstitute Austin of Computational EngineeringDec2008 and Sciences 2/32 University Outline 1 The Hodge Decomposition for smooth differential forms 2 The Hodge Decomposition for discrete differential forms 3 Applications of the Hodge Decomposition to biological modeling university-logo Computational Visualization Center , I C E S ( DepartmentThe University of Mathematics, of Texas atInstitute Austin of Computational EngineeringDec2008 and Sciences 3/32 University Outline 1 The Hodge Decomposition for smooth differential forms 2 The Hodge Decomposition for discrete differential forms 3 Applications of the Hodge Decomposition to biological modeling university-logo Computational Visualization Center , I C E S ( DepartmentThe University of Mathematics, of Texas atInstitute Austin of Computational EngineeringDec2008 and Sciences 4/32 University Differential Forms Let Ω denote a smooth n-manifold and Tx (Ω) the tangent space of Ω at x. -
5.5 Volumes: Tubes 435
5.5 volumes: tubes 435 5.5 Volumes: Tubes In Section 5.2, we devised the “disk” method to find the volume swept out when a region is revolved about a line. To find the volume swept out when revolving a region about the x-axis (see margin), we made cuts perpendicular to the x-axis so that each slice was (approximately) a “disk” with volume p (radius)2 · (thickness). Adding the volumes of these slices together yielded a Riemann sum. Taking a limit as the thicknesses of the slices approached 0, we obtained a definite integral representation for the exact volume that had the form: Z b p [ f (x)]2 dx a The disk method, while useful in many circumstances, can be cumber- some if we want to find the volume when a region defined by a curve of the form y = f (x) is revolved about the y-axis or some other vertical Refer to Examples 6(b) and 6(c) from Sec- line. To revolve the region about the y-axis, the disk method requires tion 5.2 to refresh your memory. that we rewrite the original equation y = f (x) as x = g(y). Sometimes y this is easy: if y = 3x then x = 3 . But sometimes it is not easy at all: if y = x + ex, then we cannot solve for x as an elementary function of y. The “Tube” Method Partition the x-axis (as we did in the “disk” method) to cut the region into thin, almost-rectangular vertical “slices.” When we revolve one of these slices about the y-axis (see below), we can approximate the volume of the resulting “tube” by cutting the “wall” of the tube and rolling it out flat: to get a thin, solid rectangular box. -
2 Grad, Div, Curl
c F. Waleffe, Math 321, 2006/10/25 15 ! 2 Grad, div, curl Consider a scalar function of a vector variable: f(!r), for instance the pressure p(!r) as a function of position, or the temperature T (!r) at point !r, etc. One way to visualize such functions is to consider isosurfaces or level sets, these are the set of all !r’s for which f(!r) = C0, for some constant C0. In cartesian coordinates !r = x !e + y !ey + z !ez and the scalar function is a function of the three coordinates f(!r) f(x, y, z), hence we can interpret an isosurface f(x, y, z) = C as a single ≡ 0 equation for the 3 unknowns x, y, z. In general, we are free to pick two of those variables, x and y 2 2 2 say, then solve the equation f(x, y, z) = C0 for z. For example, the isosurfaces of f(!r) = x +y +z are determined by the equation f(!r) = C . This is the sphere or radius √C , if C 0. 0 0 0 ≥ 2.1 Geometric definition of the Gradient The value of f(!r) at a point !r0 defines an isosurface f(!r) = f(!r0) through that point !r0. The gradient of f(!r) at !r can be defined geometrically as the vector, denoted ! f(!r ), that 0 ∇ 0 (i) is perpendicular to the isosurface f(!r) = f(!r0) at the point !r0 and points in the direction of increase of f(!r) and (ii) has a magnitude equal to the rate of change of f(!r) with distance from the isosurface. -
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.