The Green-Function Transform and Wave Propagation
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Approximate Separability of Green's Function for High Frequency
APPROXIMATE SEPARABILITY OF GREEN'S FUNCTION FOR HIGH FREQUENCY HELMHOLTZ EQUATIONS BJORN¨ ENGQUIST AND HONGKAI ZHAO Abstract. Approximate separable representations of Green's functions for differential operators is a basic and an important aspect in the analysis of differential equations and in the development of efficient numerical algorithms for solving them. Being able to approx- imate a Green's function as a sum with few separable terms is equivalent to the existence of low rank approximation of corresponding discretized system. This property can be explored for matrix compression and efficient numerical algorithms. Green's functions for coercive elliptic differential operators in divergence form have been shown to be highly separable and low rank approximation for their discretized systems has been utilized to develop efficient numerical algorithms. The case of Helmholtz equation in the high fre- quency limit is more challenging both mathematically and numerically. In this work, we develop a new approach to study approximate separability for the Green's function of Helmholtz equation in the high frequency limit based on an explicit characterization of the relation between two Green's functions and a tight dimension estimate for the best linear subspace approximating a set of almost orthogonal vectors. We derive both lower bounds and upper bounds and show their sharpness for cases that are commonly used in practice. 1. Introduction Given a linear differential operator, denoted by L, the Green's function, denoted by G(x; y), is defined as the fundamental solution in a domain Ω ⊆ Rn to the partial differ- ential equation 8 n < LxG(x; y) = δ(x − y); x; y 2 Ω ⊆ R (1) : with boundary condition or condition at infinity; where δ(x − y) is the Dirac delta function denoting an impulse source point at y. -
Waves and Imaging Class Notes - 18.325
Waves and Imaging Class notes - 18.325 Laurent Demanet Draft December 20, 2016 2 Preface In the margins of this text we use • the symbol (!) to draw attention when a physical assumption or sim- plification is made; and • the symbol ($) to draw attention when a mathematical fact is stated without proof. Thanks are extended to the following people for discussions, suggestions, and contributions to early drafts: William Symes, Thibaut Lienart, Nicholas Maxwell, Pierre-David Letourneau, Russell Hewett, and Vincent Jugnon. These notes are accompanied by computer exercises in Python, that show how to code the adjoint-state method in 1D, in a step-by-step fash- ion, from scratch. They are provided by Russell Hewett, as part of our software platform, the Python Seismic Imaging Toolbox (PySIT), available at http://pysit.org. 3 4 Contents 1 Wave equations 9 1.1 Physical models . .9 1.1.1 Acoustic waves . .9 1.1.2 Elastic waves . 13 1.1.3 Electromagnetic waves . 17 1.2 Special solutions . 19 1.2.1 Plane waves, dispersion relations . 19 1.2.2 Traveling waves, characteristic equations . 24 1.2.3 Spherical waves, Green's functions . 29 1.2.4 The Helmholtz equation . 34 1.2.5 Reflected waves . 35 1.3 Exercises . 39 2 Geometrical optics 45 2.1 Traveltimes and Green's functions . 45 2.2 Rays . 49 2.3 Amplitudes . 52 2.4 Caustics . 54 2.5 Exercises . 55 3 Scattering series 59 3.1 Perturbations and Born series . 60 3.2 Convergence of the Born series (math) . 63 3.3 Convergence of the Born series (physics) . -
Summary of Wave Optics Light Propagates in Form of Waves Wave
Summary of Wave Optics light propagates in form of waves wave equation in its simplest form is linear, which gives rise to superposition and separation of time and space dependence (interference, diffraction) waves are characterized by wavelength and frequency propagation through media is characterized by refractive index n, which describes the change in phase velocity media with refractive index n alter velocity, wavelength and wavenumber but not frequency lenses alter the curvature of wavefronts Optoelectronic, 2007 – p.1/25 Syllabus 1. Introduction to modern photonics (Feb. 26), 2. Ray optics (lens, mirrors, prisms, et al.) (Mar. 7, 12, 14, 19), 3. Wave optics (plane waves and interference) (Mar. 26, 28), 4. Beam optics (Gaussian beam and resonators) (Apr. 9, 11, 16), 5. Electromagnetic optics (reflection and refraction) (Apr. 18, 23, 25), 6. Fourier optics (diffraction and holography) (Apr. 30, May 2), Midterm (May 7-th), 7. Crystal optics (birefringence and LCDs) (May 9, 14), 8. Waveguide optics (waveguides and optical fibers) (May 16, 21), 9. Photon optics (light quanta and atoms) (May 23, 28), 10. Laser optics (spontaneous and stimulated emissions) (May 30, June 4), 11. Semiconductor optics (LEDs and LDs) (June 6), 12. Nonlinear optics (June 18), 13. Quantum optics (June 20), Final exam (June 27), 14. Semester oral report (July 4), Optoelectronic, 2007 – p.2/25 Paraxial wave approximation paraxial wave = wavefronts normals are paraxial rays U(r) = A(r)exp(−ikz), A(r) slowly varying with at a distance of λ, paraxial Helmholtz equation -
Fourier Optics
Fourier Optics 1 Background Ray optics is a convenient tool to determine imaging characteristics such as the location of the image and the image magni¯cation. A complete description of the imaging system, however, requires the wave properties of light and associated processes like di®raction to be included. It is these processes that determine the resolution of optical devices, the image contrast, and the e®ect of spatial ¯lters. One possible wave-optical treatment considers the Fourier spectrum (space of spatial frequencies) of the object and the transmission of the spectral components through the optical system. This is referred to as Fourier Optics. A perfect imaging system transmits all spectral frequencies equally. Due to the ¯nite size of apertures (for example the ¯nite diameter of the lens aperture) certain spectral components are attenuated resulting in a distortion of the image. Specially designed ¯lters on the other hand can be inserted to modify the spectrum in a prede¯ned manner to change certain image features. In this lab you will learn the principles of spatial ¯ltering to (i) improve the quality of laser beams, and to (ii) remove undesired structures from images. To prepare for the lab, you should review the wave-optical treatment of the imaging process (see [1], Chapters 6.3, 6.4, and 7.3). 2 Theory 2.1 Spatial Fourier Transform Consider a two-dimensional object{ a slide, for instance that has a ¯eld transmission function f(x; y). This transmission function carries the information of the object. A (mathematically) equivalent description of this object in the Fourier space is based on the object's amplitude spectrum ZZ 1 F (u; v) = f(x; y)ei2¼ux+i2¼vydxdy; (1) (2¼)2 where the Fourier coordinates (u; v) have units of inverse length and are called spatial frequencies. -
Diffraction Fourier Optics
Lab 3: Diffraction & Fourier Optics This week in lab, we will continue our study of wave optics by looking at diffraction and Fourier optics. As you may recall from Lab 1, the Fourier transform gives us a way to go back and forth between time domain and frequency domain. Here we will explore how Fourier transforms are useful in optics. Learning Objectives: In this lab, students will: • Learn how to understand diffraction by extending the concept of interference (Huygens' principle) • Learn how spatial Fourier transforms arise in optical systems: o Lenses o Diffraction gratings. • Learn how to change an image by manipulating its Fourier transform. Huygen’s Principle Think back to the Pre-Lab assignment. You played around with the wave simulator (http://falstad.com/ripple/). Here you saw that we can create waves by arranging point sources in a certain way. It's done in technological applications like antenna arrays where you want to create a radio wave going in a certain direction. In ultrasound probes of the body an array of acoustic sources is used to create a wave that focuses at a certain point. In the simulation we used sources with the same phase, but if you vary the phase, you can also move the focused point around. In 1678, Huygens figured out that you could calculate how a wave propagates by making a cut in space and measuring the phase and amplitude everywhere on that plane. Then you place point emitters on the plane with the same amplitude and phase as you measured. The radiation from these point sources adds up to exactly the same wave you had coming in (Figure 1). -
15C Sp16 Fourier Intro
Outline 1. Ray Optics Review 1. Image formation (slide 2) 2. Special cases of plane wave or point source (slide 3) 3. Pinhole Camera (slide 4) 2. Wave Optics Review 1. Definitions and plane wave example (slides 5,6) 2. Point sources and spherical waves (slide 7,8) 3. Introduction to Huygen’s wavelets 1. Plane wave example (slide 9) 4. Transmission of a plane wave through a single slit 1. Narrow slits and Huygen’s wavelets (slides 10,11) 2. Wider slits up to the geometric optics limit (slide 12) 3. Time average intensity patterns from single slits (slide 13) 5. Transmission of a plane wave through two slits 1. Narrow slits and Huygen’s wavelets (slides 14-17) 2. Calculating the time averaged intensity pattern observed on a screen (slides 20-22) 6. Wave view of lenses (slides 24,25) 7. 4f optical system and Fourier Transforms 1. Introduction (slide 26) and reminder that the laser produces a plane wave in the z direction, so the kx and ky components of the propagation vector are 0 2. Demonstration that the field amplitude pattern g(x,y) produced by the LCD results in diffraction that creates non-zero k vector components in the x and y directions such that the amplitude of the efield in the fourier transform plane corresponds to the kx ky components of the fourier transform of g(x,y) Lec 2 :1 Ray Optics Intro Assumption: Light travels in straight lines Ray optics diagrams show the straight lines along which the light propagates Rules for a converging lens with focal length f, with the optic axis defined as the line that goes through the center of the lens perpendicular to the plane containing the lens: 1. -
Fourier Optics
Fourier optics 15-463, 15-663, 15-862 Computational Photography http://graphics.cs.cmu.edu/courses/15-463 Fall 2017, Lecture 28 Course announcements • Any questions about homework 6? • Extra office hours today, 3-5pm. • Make sure to take the three surveys: 1) faculty course evaluation 2) TA evaluation survey 3) end-of-semester class survey • Monday are project presentations - Do you prefer 3 minutes or 6 minutes per person? - Will post more details on Piazza. - Also please return cameras on Monday! Overview of today’s lecture • The scalar wave equation. • Basic waves and coherence. • The plane wave spectrum. • Fraunhofer diffraction and transmission. • Fresnel lenses. • Fraunhofer diffraction and reflection. Slide credits Some of these slides were directly adapted from: • Anat Levin (Technion). Scalar wave equation Simplifying the EM equations Scalar wave equation: • Homogeneous and source-free medium • No polarization 1 휕2 훻2 − 푢 푟, 푡 = 0 푐2 휕푡2 speed of light in medium Simplifying the EM equations Helmholtz equation: • Either assume perfectly monochromatic light at wavelength λ • Or assume different wavelengths independent of each other 훻2 + 푘2 ψ 푟 = 0 2휋푐 −푗 푡 푢 푟, 푡 = 푅푒 ψ 푟 푒 휆 what is this? ψ 푟 = 퐴 푟 푒푗휑 푟 Simplifying the EM equations Helmholtz equation: • Either assume perfectly monochromatic light at wavelength λ • Or assume different wavelengths independent of each other 훻2 + 푘2 ψ 푟 = 0 Wave is a sinusoid at frequency 2휋/휆: 2휋푐 −푗 푡 푢 푟, 푡 = 푅푒 ψ 푟 푒 휆 ψ 푟 = 퐴 푟 푒푗휑 푟 what is this? Simplifying the EM equations Helmholtz equation: -
Convolution Quadrature for the Wave Equation with Impedance Boundary Conditions
Convolution Quadrature for the Wave Equation with Impedance Boundary Conditions a b, S.A. Sauter , M. Schanz ∗ aInstitut f¨urMathematik, University Zurich, Winterthurerstrasse 190, CH-8057 Z¨urich, Switzerland, e-mail: [email protected] bInstitute of Applied Mechanics, Graz University of Technology, 8010 Graz, Austria, e-mail: [email protected] Abstract We consider the numerical solution of the wave equation with impedance bound- ary conditions and start from a boundary integral formulation for its discretiza- tion. We develop the generalized convolution quadrature (gCQ) to solve the arising acoustic retarded potential integral equation for this impedance prob- lem. For the special case of scattering from a spherical object, we derive rep- resentations of analytic solutions which allow to investigate the effect of the impedance coefficient on the acoustic pressure analytically. We have performed systematic numerical experiments to study the convergence rates as well as the sensitivity of the acoustic pressure from the impedance coefficients. Finally, we apply this method to simulate the acoustic pressure in a building with a fairly complicated geometry and to study the influence of the impedance coefficient also in this situation. Keywords: generalized Convolution quadrature, impedance boundary condition, boundary integral equation, time domain, retarded potentials 1. Introduction The efficient and reliable simulation of scattered waves in unbounded exterior domains is a numerical challenge and the development of fast numerical methods ∗Corresponding author Preprint submitted to Computer Methods in Applied Mechanics and EngineeringAugust 18, 2016 is far from being matured. We are interested in boundary integral formulations 5 of the problem to avoid the use of an artificial boundary with approximate transmission conditions [1, 2, 3, 4, 5] and to allow for recasting the problem (under certain assumptions which will be detailed later) as an integral equation on the surface of the scatterer. -
Fourier Optics
Phys 322 Chapter 11 Lecture 31 Fourier Optics Introduction Fourier Transform: definition and properties Fourier optics Introduction Motivation: 1) To understand how an image is formed. 2) To evaluate the quality of an optical system. Optical system Star (Telescope) Film Delta function Fourier transform Inverse Fourier and filtering transform The quality of the optical system: • The image is not as sharp as the object because some high frequencies are lost. • The filtering function determines the quality of the optical system. Fourier transform: 1D case Any function can be represented as a Fourier integral: angular spatial frequency k 2 / 1 f x Ak cos kx dk B k sin kx dk 00 Fourier sine transform of f(x) Fourier cosine transform of f(x) Ak f x cos kx dx Bk f x sin kx dx Alternatively, it could be represented in complex form: 1 f x Fk eikxdk complex 2 functions! ikx Fk f x e dx Fk A k iBk Fkei k Fourier transform 1 F(k) is Fourier transform of f(x): f x Fk eikxdk 2 Ffk Y x Fk f x eikxdx f(x) is inverse Fourier transform of F(k): -1 fx Y Fk Application to waves: 1 If f(t) is shape of a wave in time: f t F eitd 2 F f t eitdt Reminder: the Fourier Transform of a rectangle function: rect(t) 1/2 1 Fitdtit( ) exp( ) [exp( )]1/2 1/2 1/2 i 1 [exp(ii / 2) exp( i exp(ii / 2) exp( 2i sin( F() Imaginary F(sinc( Component = 0 Sinc(x) and why it's important Sinc(x/2) is the Fourier transform of a rectangle function. -
5. GAUSSIAN BEAMS 5.1. Solution to the Wave Equation in Cartesian Coordinates
5. GAUSSIAN BEAMS 5.1. Solution to the wave equation in Cartesian coordinates • Recall the Helmholtz equation for a scalar field U in rectangular coordinates ∇+22UU(r,ωβ) ( rr , ω )( , ω) = 0, (5.1) • β is the wavenumber, defined as β22(rrr,,, ω) = ω µε( ω) − i ωµσ( ω ) ω 2 (5.2) = ni2 (rr,,ω) − ωµσ( ω ) c2 • Assuming lossless medium (σ = 0) and decoupling the vacuum contribution (n =1) from βω(r, ), we re-write Eq. 2 to explicitly show the driving term, ∇+2 ω2 ω =−−22 ωω U(r,,) kU00( r) k n( rr,) 1 U( ,,) (5.3) • k = ω . 0 c 1 • Eq. 3 preserves the generality of the Helmholtz equation. Green’s function, h, (an impulse response) is obtained by setting the driving term to a delta function, ∇+2h(rr,,ω) kh2 ( ωδ) =−(3) ( r) 0 (5.4) = −δδδ( xyz) ( ) ( ) • To solve this equation, we take the Fourier transform with respect to r, 22 −+kh(kk,ωω) k0 h( ,1) =−, (5.5) • k 2 =kk ⋅ . • This equation breaks into three identical equations, for each spatial coordinate, 1 hk( x ,ω) = 22 kkx − 0 (5.6) 11 1 = − 2k000xx kk−+ kk x 2 • To calculate the Fourier transform of Eq. 6, we invoke the shift theorem and the Fourier transform of function 1 , k fk( −→ a) eiax ⋅ fx ( ) 1 (5.7) → ixsign( ) k • Thus we obtain as the final solution 1 hx( ,ω) =⋅> eik0 x ,0 x (5.8) ik0 • The procedure applies to all three dimensions, such that the 3D solution reads ⋅ he(r,ω) ∝ ik0 kr, (5.9) • k is the unit vector, kk = / k . -
Fundamentals of Modern Optics", FSU Jena, Prof
Script "Fundamentals of Modern Optics", FSU Jena, Prof. T. Pertsch, FoMO_Script_2017-10-10s.docx 1 Fundamentals of Modern Optics Winter Term 2017/2018 Prof. Thomas Pertsch Abbe School of Photonics, Friedrich-Schiller-Universität Jena Table of content 0. Introduction ............................................................................................... 4 1. Ray optics - geometrical optics (covered by lecture Introduction to Optical Modeling) .............................................................................................. 15 1.1 Introduction ......................................................................................................... 15 1.2 Postulates ........................................................................................................... 15 1.3 Simple rules for propagation of light ................................................................... 16 1.4 Simple optical components ................................................................................. 16 1.5 Ray tracing in inhomogeneous media (graded-index - GRIN optics) .................. 20 1.5.1 Ray equation ............................................................................................ 20 1.5.2 The eikonal equation ................................................................................ 22 1.6 Matrix optics ........................................................................................................ 23 1.6.1 The ray-transfer-matrix ............................................................................ -
Fundamental Course: Fourier Optics
Fundamental course: Fourier Optics Lecture notes Revision date: August 27, 2018 Simulation of the Point-Spread Function of a 39 pupils interferometer, with sub-apertures disposed on 3 concentric rings. Contents 0 Reminders about Fourier analysis 4 0.1 Some useful functions . .4 0.1.1 The rectangle function . .4 0.1.2 Dirac delta distribution . .5 0.1.3 Dirac comb . .6 0.2 Convolution . .7 0.2.1 Definition . .7 0.2.2 Properties . .7 0.3 Fourier transform . .9 0.3.1 Definition . .9 0.3.2 Properties . 10 0.3.3 Table of Fourier transforms . 11 1 Reminders about diffraction 12 1.1 Some particular kinds of waves . 12 1.1.1 Monochromatic waves . 12 1.1.2 Plane waves . 12 1.1.3 Spherical waves . 13 1.2 Huyghens-Fresnel principle . 16 1.2.1 Introduction . 16 1.2.2 Paraxial approximation and Fresnel diffraction . 16 1.2.3 Far field: Fraunhofer diffraction . 17 1.2.4 Example of Fraunhofer diffraction patterns . 18 2 Fourier properties of converging lenses 22 2.1 Phase screens . 22 2.1.1 Transmission coefficient of a thin phase screen . 22 2.1.2 Example: prism . 23 2.1.3 Converging lens . 23 2.2 Converging lenses and Fourier transform . 24 2.2.1 Object in the same plane as the lens . 25 2.2.2 Object at the front focal plane . 25 3 Coherent optical filtering 27 3.1 Principle . 27 3.2 Abbe-Porter experiments . 27 3.3 Object-image relation . 29 3.4 Low-pass and high-pass filters .