Introduction to the Special Functions of Mathematical Physics
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Mathematics 412 Partial Differential Equations
Mathematics 412 Partial Differential Equations c S. A. Fulling Fall, 2005 The Wave Equation This introductory example will have three parts.* 1. I will show how a particular, simple partial differential equation (PDE) arises in a physical problem. 2. We’ll look at its solutions, which happen to be unusually easy to find in this case. 3. We’ll solve the equation again by separation of variables, the central theme of this course, and see how Fourier series arise. The wave equation in two variables (one space, one time) is ∂2u ∂2u = c2 , ∂t2 ∂x2 where c is a constant, which turns out to be the speed of the waves described by the equation. Most textbooks derive the wave equation for a vibrating string (e.g., Haber- man, Chap. 4). It arises in many other contexts — for example, light waves (the electromagnetic field). For variety, I shall look at the case of sound waves (motion in a gas). Sound waves Reference: Feynman Lectures in Physics, Vol. 1, Chap. 47. We assume that the gas moves back and forth in one dimension only (the x direction). If there is no sound, then each bit of gas is at rest at some place (x,y,z). There is a uniform equilibrium density ρ0 (mass per unit volume) and pressure P0 (force per unit area). Now suppose the gas moves; all gas in the layer at x moves the same distance, X(x), but gas in other layers move by different distances. More precisely, at each time t the layer originally at x is displaced to x + X(x,t). -
Integration in Terms of Exponential Integrals and Incomplete Gamma Functions
Integration in terms of exponential integrals and incomplete gamma functions Waldemar Hebisch Wrocªaw University [email protected] 13 July 2016 Introduction What is FriCAS? I FriCAS is an advanced computer algebra system I forked from Axiom in 2007 I about 30% of mathematical code is new compared to Axiom I about 200000 lines of mathematical code I math code written in Spad (high level strongly typed language very similar to FriCAS interactive language) I runtime system currently based on Lisp Some functionality added to FriCAS after fork: I several improvements to integrator I limits via Gruntz algorithm I knows about most classical special functions I guessing package I package for computations in quantum probability I noncommutative Groebner bases I asymptotically fast arbitrary precision computation of elliptic functions and elliptic integrals I new user interfaces (Emacs mode and Texmacs interface) FriCAS inherited from Axiom good (probably the best at that time) implementation of Risch algorithm (Bronstein, . ) I strong for elementary integration I when integral needed special functions used pattern matching Part of motivation for current work came from Rubi testsuite: Rubi showed that there is a lot of functions which are integrable in terms of relatively few special functions. I Adapting Rubi looked dicult I Previous work on adding special functions to Risch integrator was widely considered impractical My conclusion: Need new algorithmic approach. Ex post I claim that for considered here class of functions extension to Risch algorithm is much more powerful than pattern matching. Informally Ei(u)0 = u0 exp(u)=u Γ(a; u)0 = u0um=k exp(−u) for a = (m + k)=k and −k < m < 0. -
Cylindrical Waves Guided Waves
Cylindrical Waves Guided Waves Cylindrical Waves Daniel S. Weile Department of Electrical and Computer Engineering University of Delaware ELEG 648—Waves in Cylindrical Coordinates D. S. Weile Cylindrical Waves 2 Guided Waves Cylindrical Waveguides Radial Waveguides Cavities Cylindrical Waves Guided Waves Outline 1 Cylindrical Waves Separation of Variables Bessel Functions TEz and TMz Modes D. S. Weile Cylindrical Waves Cylindrical Waves Guided Waves Outline 1 Cylindrical Waves Separation of Variables Bessel Functions TEz and TMz Modes 2 Guided Waves Cylindrical Waveguides Radial Waveguides Cavities D. S. Weile Cylindrical Waves Separation of Variables Cylindrical Waves Bessel Functions Guided Waves TEz and TMz Modes Outline 1 Cylindrical Waves Separation of Variables Bessel Functions TEz and TMz Modes 2 Guided Waves Cylindrical Waveguides Radial Waveguides Cavities D. S. Weile Cylindrical Waves Separation of Variables Cylindrical Waves Bessel Functions Guided Waves TEz and TMz Modes The Scalar Helmholtz Equation Just as in Cartesian coordinates, Maxwell’s equations in cylindrical coordinates will give rise to a scalar Helmholtz Equation. We study it first. r2 + k 2 = 0 In cylindrical coordinates, this becomes 1 @ @ 1 @2 @2 ρ + + + k 2 = 0 ρ @ρ @ρ ρ2 @φ2 @z2 We will solve this by separating variables: = R(ρ)Φ(φ)Z(z) D. S. Weile Cylindrical Waves Separation of Variables Cylindrical Waves Bessel Functions Guided Waves TEz and TMz Modes Separation of Variables Substituting and dividing by , we find 1 d dR 1 d2Φ 1 d2Z ρ + + + k 2 = 0 ρR dρ dρ ρ2Φ dφ2 Z dz2 The third term is independent of φ and ρ, so it must be constant: 1 d2Z = −k 2 Z dz2 z This leaves 1 d dR 1 d2Φ ρ + + k 2 − k 2 = 0 ρR dρ dρ ρ2Φ dφ2 z D. -
Calculus 1 to 4 (2004–2006)
Calculus 1 to 4 (2004–2006) Axel Schuler¨ January 3, 2007 2 Contents 1 Real and Complex Numbers 11 Basics .......................................... 11 Notations ..................................... 11 SumsandProducts ................................ 12 MathematicalInduction. 12 BinomialCoefficients............................... 13 1.1 RealNumbers................................... 15 1.1.1 OrderedSets ............................... 15 1.1.2 Fields................................... 17 1.1.3 OrderedFields .............................. 19 1.1.4 Embedding of natural numbers into the real numbers . ....... 20 1.1.5 The completeness of Ê .......................... 21 1.1.6 TheAbsoluteValue............................ 22 1.1.7 SupremumandInfimumrevisited . 23 1.1.8 Powersofrealnumbers.. .. .. .. .. .. .. 24 1.1.9 Logarithms ................................ 26 1.2 Complexnumbers................................. 29 1.2.1 TheComplexPlaneandthePolarform . 31 1.2.2 RootsofComplexNumbers . 33 1.3 Inequalities .................................... 34 1.3.1 MonotonyofthePowerand ExponentialFunctions . ...... 34 1.3.2 TheArithmetic-Geometricmeaninequality . ...... 34 1.3.3 TheCauchy–SchwarzInequality . .. 35 1.4 AppendixA.................................... 36 2 Sequences and Series 43 2.1 ConvergentSequences .. .. .. .. .. .. .. .. 43 2.1.1 Algebraicoperationswithsequences. ..... 46 2.1.2 Somespecialsequences . .. .. .. .. .. .. 49 2.1.3 MonotonicSequences .. .. .. .. .. .. .. 50 2.1.4 Subsequences............................... 51 2.2 CauchySequences -
Appendix a FOURIER TRANSFORM
Appendix A FOURIER TRANSFORM This appendix gives the definition of the one-, two-, and three-dimensional Fourier transforms as well as their properties. A.l One-Dimensional Fourier Transform If we have a one-dimensional (1-D) function fix), its Fourier transform F(m) is de fined as F(m) = [ f(x)exp( -2mmx)dx, (A.l.l) where m is a variable in the Fourier space. Usually it is termed the frequency in the Fou rier domain. If xis a variable in the spatial domain, miscalled the spatial frequency. If x represents time, then m is the temporal frequency which denotes, for example, the colour of light in optics or the tone of sound in acoustics. In this book, we call Eq. (A.l.l) the inverse Fourier transform because a minus sign appears in the exponent. Eq. (A.l.l) can be symbolically expressed as F(m) =,r 1{Jtx)}. (A.l.2) where ~-~denotes the inverse Fourier transform in Eq. (A.l.l). Therefore, the direct Fourier transform in this case is given by f(x) = r~ F(m)exp(2mmx)dm, (A.1.3) which can be symbolically rewritten as fix)= ~{F(m)}. (A.l.4) Substituting Eq. (A.l.2) into Eq. (A.l.4) results in 200 Appendix A ftx) = 1"1"-'{.ftx)}. (A.l.5) Therefore we have the following unity relation: ,.,... = l. (A.l.6) It means that performing a direct Fourier transform and an inverse Fourier transform of a function.ftx) successively leads to no change. Using the identity exp(ix) = cosx + isinx and Eq. -
Hydrology 510 Quantitative Methods in Hydrology
New Mexico Tech Hyd 510 Hydrology Program Quantitative Methods in Hydrology Hydrology 510 Quantitative Methods in Hydrology Motive Preview: Example of a function and its limits Consider the solute concentration, C [ML-3], in a confined aquifer downstream of a continuous source of solute with fixed concentration, C0, starting at time t=0. Assume that the solute advects in the aquifer while diffusing into the bounding aquitards, above and below (but which themselves have no significant flow). (A homology to this problem would be solute movement in a fracture aquitard (diffusion controlled) Flow aquifer Solute (advection controlled) aquitard (diffusion controlled) x with diffusion into the porous-matrix walls bounding the fracture: the so-called “matrix diffusion” problem. The difference with the original problem is one of spatial scale.) An approximate solution for this conceptual model, describing the space-time variation of concentration in the aquifer, is the function: 1/ 2 x x D C x D C(x,t) = C0 erfc or = erfc B vt − x vB C B v(vt − x) 0 where t is time [T] since the solute was first emitted x is the longitudinal distance [L] downstream, v is the longitudinal groundwater (seepage) velocity [L/T] in the aquifer, D is the effective molecular diffusion coefficient in the aquitard [L2/T], B is the aquifer thickness [L] and erfc is the complementary error function. Describe the behavior of this function. Pick some typical numbers for D/B2 (e.g., D ~ 10-9 m2 s-1, typical for many solutes, and B = 2m) and v (e.g., 0.1 m d-1), and graph the function vs. -
Chapter 9 Some Special Functions
Chapter 9 Some Special Functions Up to this point we have focused on the general properties that are associated with uniform convergence of sequences and series of functions. In this chapter, most of our attention will focus on series that are formed from sequences of functions that are polynomials having one and only one zero of increasing order. In a sense, these are series of functions that are “about as good as it gets.” It would be even better if we were doing this discussion in the “Complex World” however, we will restrict ourselves mostly to power series in the reals. 9.1 Power Series Over the Reals jIn this section,k we turn to series that are generated by sequences of functions :k * ck x k0. De¿nition 9.1.1 A power series in U about the point : + U isaseriesintheform ;* n c0 cn x : n1 where : and cn, for n + M C 0 , are real constants. Remark 9.1.2 When we discuss power series, we are still interested in the differ- ent types of convergence that were discussed in the last chapter namely, point- 3wise, uniform and absolute. In this context, for example, the power series c0 * :n t U n1 cn x is said to be3pointwise convergent on a set S 3if and only if, + * :n * :n for each x0 S, the series c0 n1 cn x0 converges. If c0 n1 cn x0 369 370 CHAPTER 9. SOME SPECIAL FUNCTIONS 3 * :n is divergent, then the power series c0 n1 cn x is said to diverge at the point x0. -
The Laplace Transform of the Modified Bessel Function K( T± M X) Where
THE LAPLACE TRANSFORM OF THE MODIFIED BESSEL FUNCTION K(t±»>x) WHERE m-1, 2, 3, ..., n by F. M. RAGAB (Received in revised form 13th August 1963) 1. Introduction In the present paper we determine the Laplace transforms of the modified ±m Bessel function of the second kind Kn(t x), where m is any positive integer. The Laplace transforms are given in (2) and (4) below, p being the transform parameter and having positive real part. The formulae to be established are as follows (l)-(4): f Jo 2m-1 2\-~ -i-,lx 2m 2m 2m J k + v - 2 2m (2m)2mx2 v+1 v + 2 v + 2m (1) 2m 2m 2m where R(k±nm)>0 and x is taken for simplicity to be real and positive" When m = 1, x may be taken to be complex with real part greater than 1. The asterisk in the generalised hypergeometric function denotes that the . 2m . ., v+1 v + 2 v + 2m . factor =— m th e parametert s , , ..., is omitted; m is a positive 2m 2m 2m 2m integer. m m 1 e~"'Kn(t x)dt = 2 ~ V f V J ±l - 2m-l 2 2m 2m 2m 2m J 2m v+1 n v+1. 4p ' 2 2m 2 1m"' (2m)2mx2 2r2m+l v+1 v+2 v + 2m (2) _ 2m 2m ~lm~ E.M.S.—Y Downloaded from https://www.cambridge.org/core. 02 Oct 2021 at 05:34:47, subject to the Cambridge Core terms of use. 326 F. M. RAGAB where m is a positive integer, R(± mn +1)>0 and R(p) > 0; x is taken to be rea and positive and the asterisk has the same meaning as before. -
Conceptual Power Series Knowledge of STEM Majors
Paper ID #21246 Conceptual Power Series Knowledge of STEM Majors Dr. Emre Tokgoz, Quinnipiac University Emre Tokgoz is currently the Director and an Assistant Professor of Industrial Engineering at Quinnipiac University. He completed a Ph.D. in Mathematics and another Ph.D. in Industrial and Systems Engineer- ing at the University of Oklahoma. His pedagogical research interest includes technology and calculus education of STEM majors. He worked on several IRB approved pedagogical studies to observe under- graduate and graduate mathematics and engineering students’ calculus and technology knowledge since 2011. His other research interests include nonlinear optimization, financial engineering, facility alloca- tion problem, vehicle routing problem, solar energy systems, machine learning, system design, network analysis, inventory systems, and Riemannian geometry. c American Society for Engineering Education, 2018 Mathematics & Engineering Majors’ Conceptual Cognition of Power Series Emre Tokgöz [email protected] Industrial Engineering, School of Engineering, Quinnipiac University, Hamden, CT, 08518 Taylor series expansion of functions has important applications in engineering, mathematics, physics, and computer science; therefore observing responses of graduate and senior undergraduate students to Taylor series questions appears to be the initial step for understanding students’ conceptual cognitive reasoning. These observations help to determine and develop a successful teaching methodology after weaknesses of the students are investigated. Pedagogical research on understanding mathematics and conceptual knowledge of physics majors’ power series was conducted in various studies ([1-10]); however, to the best of our knowledge, Taylor series knowledge of engineering majors was not investigated prior to this study. In this work, the ability of graduate and senior undergraduate engineering and mathematics majors responding to a set of power series questions are investigated. -
Non-Classical Integrals of Bessel Functions
J. Austral. Math. Soc. (Series E) 22 (1981), 368-378 NON-CLASSICAL INTEGRALS OF BESSEL FUNCTIONS S. N. STUART (Received 14 August 1979) (Revised 20 June 1980) Abstract Certain definite integrals involving spherical Bessel functions are treated by relating them to Fourier integrals of the point multipoles of potential theory. The main result (apparently new) concerns where /,, l2 and N are non-negative integers, and r, and r2 are real; it is interpreted as a generalized function derived by differential operations from the delta function 2 fi(r, — r^. An ancillary theorem is presented which expresses the gradient V "y/m(V) of a spherical harmonic function g(r)YLM(U) in a form that separates angular and radial variables. A simple means of translating such a function is also derived. 1. Introduction Some of the best-known differential equations of mathematical physics lead to Fourier integrals that involve Bessel functions when, for instance, they are solved under conditions of rotational symmetry about a centre. As long as the integrals are absolutely convergent they are amenable to the classical analysis of Watson's standard treatise, but a calculus of wider serviceability can be expected from the theory of generalized functions. This paper identifies a class of Bessel-function integrals that are Fourier integrals of derivatives of the Dirac delta function in three dimensions. They arise in the practical context of manipulating special functions-notably when changing the origin of the polar coordinates, for example, in order to evaluate so-called two-centre integrals and ©Copyright Australian Mathematical Society 1981 368 Downloaded from https://www.cambridge.org/core. -
Multiple-Precision Exponential Integral and Related Functions
Multiple-Precision Exponential Integral and Related Functions David M. Smith Loyola Marymount University This article describes a collection of Fortran-95 routines for evaluating the exponential integral function, error function, sine and cosine integrals, Fresnel integrals, Bessel functions and related mathematical special functions using the FM multiple-precision arithmetic package. Categories and Subject Descriptors: G.1.0 [Numerical Analysis]: General { computer arith- metic, multiple precision arithmetic; G.1.2 [Numerical Analysis]: Approximation { special function approximation; G.4 [Mathematical Software]: { Algorithm design and analysis, efficiency, portability General Terms: Algorithms, exponential integral function, multiple precision Additional Key Words and Phrases: Accuracy, function evaluation, floating point, Fortran, math- ematical library, portable software 1. INTRODUCTION The functions described here are high-precision versions of those in the chapters on the ex- ponential integral function, error function, sine and cosine integrals, Fresnel integrals, and Bessel functions in a reference such as Abramowitz and Stegun [1965]. They are Fortran subroutines that use the FM package [Smith 1991; 1998; 2001] for multiple-precision arithmetic, constants, and elementary functions. The FM package supports high precision real, complex, and integer arithmetic and func- tions. All the standard elementary functions are included, as well as about 25 of the mathematical special functions. An interface module provides easy use of the package through three multiple- precision derived types, and also extends Fortran's array operations involving vectors and matrices to multiple-precision. The routines in this module can detect an attempt to use a multiple precision variable that has not been defined, which helps in debugging. There is great flexibility in choosing the numerical environment of the package. -
Radial Functions and the Fourier Transform
Radial functions and the Fourier transform Notes for Math 583A, Fall 2008 December 6, 2008 1 Area of a sphere The volume in n dimensions is n n−1 n−1 vol = d x = dx1 ··· dxn = r dr d ω. (1) Here r = |x| is the radius, and ω = x/r it a radial unit vector. Also dn−1ω denotes the angular integral. For instance, when n = 2 it is dθ for 0 ≤ θ ≤ 2π, while for n = 3 it is sin(θ) dθ dφ for 0 ≤ θ ≤ π and 0 ≤ φ ≤ 2π. The radial component of the volume gives the area of the sphere. The radial directional derivative along the unit vector ω = x/r may be denoted 1 ∂ ∂ ∂ ωd = (x1 + ··· + xn ) = . (2) r ∂x1 ∂xn ∂r The corresponding spherical area is ωdcvol = rn−1 dn−1ω. (3) Thus when n = 2 it is (1/r)(x dy−y dx) = r dθ, while for n = 3 it is (1/r)(x dy dz+ y dz dx + x dx dy) = r2 sin(θ) dθ dφ. The divergence theorem for the ball Br of radius r is thus Z Z div v dnx = v · ω rn−1dn−1ω. (4) Br Sr Notice that if one takes v = x, then div x = n, while x · ω = r. This shows that n n times the volume of the ball is r times the surface areaR of the sphere. ∞ z −t dt Recall that the Gamma function is defined by Γ(z) = 0 t e t . It is easy to show that Γ(z + 1) = zΓ(z).