Differential Algebra (DA) based Fast Multipole Method (FMM)

Similar documents
He Zhang (now Jefferson Lab), Zhensheng Tao, Jenni Portman, Phillip Duxbury, Chong-Yu Ruan, Kyoko Makino, Martin Berz. Michigan State University

Multiple Level Fast Multipole Algorithm in Differential Algebra Frame and Its Prospective Usage in Photoemission Simulation

A Crash Course on Fast Multipole Method (FMM)

18.336J/6.335J: Fast methods for partial differential and integral equations. Lecture 13. Instructor: Carlos Pérez Arancibia

Fast multipole method

An implementation of the Fast Multipole Method without multipoles

MIL-DTL-5015 Style Circular Connectors

COSY INFINITY Version 7

Fast Multipole Methods for The Laplace Equation. Outline

An Introduction to Differential Algebra

INTRODUCTION TO FAST MULTIPOLE METHODS LONG CHEN

Scalability Metrics for Parallel Molecular Dynamics

Math 110 Midterm 1 Study Guide October 14, 2013

Notation Nodes are data points at which functional values are available or at which you wish to compute functional values At the nodes fx i

High-Order Representation of Poincaré Maps

Algebraic Expressions

Fast multipole boundary element method for the analysis of plates with many holes

A fast method for solving the Heat equation by Layer Potentials

CALCULUS JIA-MING (FRANK) LIOU

Formal groups. Peter Bruin 2 March 2006

Section 5.1 Composite Functions

This exam contains 9 problems. CHECK THAT YOU HAVE A COMPLETE EXAM.

Polynomial Degree Leading Coefficient. Sign of Leading Coefficient

Convergence of sequences and series

Chapter 8. Exploring Polynomial Functions. Jennifer Huss

LECTURE 5, FRIDAY

LECTURE 16 GAUSS QUADRATURE In general for Newton-Cotes (equispaced interpolation points/ data points/ integration points/ nodes).

Formal Groups. Niki Myrto Mavraki

MATH 228: Calculus III (FALL 2016) Sample Problems for FINAL EXAM SOLUTIONS

Fourth Week: Lectures 10-12

Lecture 2: The Fast Multipole Method

Lecture 10: Predicate Logic and Its Language

From Particle Accelerators to Celestial Dynamics: An Introduction to Differential Algebra

Handout 5, Summer 2014 Math May Consider the following table of values: x f(x) g(x) f (x) g (x)

Numerical Methods in Physics and Astrophysics

Fast Algorithms for Many-Particle Simulations on Peta-Scale Parallel Architectures

COSY INFINITY version 8

July 21 Math 2254 sec 001 Summer 2015

1) The line has a slope of ) The line passes through (2, 11) and. 6) r(x) = x + 4. From memory match each equation with its graph.

STAT 450: Statistical Theory. Distribution Theory. Reading in Casella and Berger: Ch 2 Sec 1, Ch 4 Sec 1, Ch 4 Sec 6.

ALGEBRAIC GEOMETRY HOMEWORK 3

Math 205b Homework 2 Solutions

Taylor Model Range Bounding Schemes

A Fast Adaptive Multipole Algorithm in Three Dimensions

Series Solutions of Differential Equations

Math 671: Tensor Train decomposition methods

ROOT FINDING REVIEW MICHELLE FENG

Definition A.1. We say a set of functions A C(X) separates points if for every x, y X, there is a function f A so f(x) f(y).

CS 542G: The Poisson Problem, Finite Differences

Course 311: Michaelmas Term 2005 Part III: Topics in Commutative Algebra

Section 9.7 and 9.10: Taylor Polynomials and Approximations/Taylor and Maclaurin Series

An adaptive fast multipole boundary element method for the Helmholtz equation

A fast adaptive numerical solver for nonseparable elliptic partial differential equations

Chapter 1- Polynomial Functions

March Algebra 2 Question 1. March Algebra 2 Question 1

8 Building New Functions from Old Ones

Name: Class: Date: PostAssessment Polynomial Unit. Multiple Choice Identify the choice that best completes the statement or answers the question.

Verified Global Optimization with Taylor Model based Range Bounders

e x2 dxdy, e x2 da, e x2 x 3 dx = e

εx 2 + x 1 = 0. (2) Suppose we try a regular perturbation expansion on it. Setting ε = 0 gives x 1 = 0,

Approximation theory

Parallel Adaptive Fast Multipole Method: application, design, and more...

PreCalculus: Semester 1 Final Exam Review

Improved Fast Gauss Transform. (based on work by Changjiang Yang and Vikas Raykar)

Subgradients. subgradients. strong and weak subgradient calculus. optimality conditions via subgradients. directional derivatives

MATH 423 Linear Algebra II Lecture 3: Subspaces of vector spaces. Review of complex numbers. Vector space over a field.

PDE: The Method of Characteristics Page 1

Section 0.2 & 0.3 Worksheet. Types of Functions

4.3 General attacks on LFSR based stream ciphers

Chapter Generating Functions

Joint Iterative Decoding of LDPC Codes and Channels with Memory

Pade Approximations and the Transcendence

Name: Class: Date: Rationals Multiple Choice Pre-Test. Multiple Choice Identify the choice that best completes the statement or answers the question.

Measures, orthogonal polynomials, and continued fractions. Michael Anshelevich

Engg. Math. I. Unit-I. Differential Calculus

MATH 304 Linear Algebra Lecture 8: Vector spaces. Subspaces.

g(x) = 1 1 x = 1 + x + x2 + x 3 + is not a polynomial, since it doesn t have finite degree. g(x) is an example of a power series.

Schemes. Philipp Keding AT15, San Antonio, TX. Quarklet Frames in Adaptive Numerical. Schemes. Philipp Keding. Philipps-University Marburg

Measures, orthogonal polynomials, and continued fractions. Michael Anshelevich

A HIERARCHICAL 3-D DIRECT HELMHOLTZ SOLVER BY DOMAIN DECOMPOSITION AND MODIFIED FOURIER METHOD

Polynomial Review Problems

Higher order derivatives of the inverse function

Transactions on Modelling and Simulation vol 18, 1997 WIT Press, ISSN X

Curve Fitting. Objectives

PART III. Outline. Codes and Cryptography. Sources. Optimal Codes (I) Jorge L. Villar. MAMME, Fall 2015

Math 32B Discussion Session Session 3 Notes August 14, 2018

Today we will prove one result from probability that will be useful in several statistical tests. ... B1 B2 Br. Figure 23.1:

Optimization using Calculus. Optimization of Functions of Multiple Variables subject to Equality Constraints

Math 40510, Algebraic Geometry

Optimization and Calculus

Need help? Try or 4.1 Practice Problems

Fast reversion of formal power series

Math 2233 Homework Set 7

ICES REPORT A Multilevel-WENO Technique for Solving Nonlinear Conservation Laws

(1) u i = g(x i, x j )q j, i = 1, 2,..., N, where g(x, y) is the interaction potential of electrostatics in the plane. 0 x = y.

Chapter 6: Rational Expr., Eq., and Functions Lecture notes Math 1010

Convergence Order Studies for Elliptic Test Problems with COMSOL Multiphysics

1 Fundamental Concepts From Algebra & Precalculus

arxiv: v1 [cs.lg] 26 Jul 2017

A Multilevel Proximal Algorithm for Large Scale Composite Convex Optimization

Transcription:

Differential Algebra (DA) based Fast Multipole Method (FMM) Department of Physics and Astronomy Michigan State University June 27, 2010

Space charge effect Space charge effect : the fields of the charged particles in a bunch on each other affect the motion of themselves. Pair-to-pair method, time consuming, O(N 2 ) Tree code, O(N log N). (The field of the electrons far away from the observer point, can be represented by the field of a multipole.) Fast multipole method, much faster, O(N). (Multipole expansion and local expansion, recursive progress.)

Space charge effect L M O(N log N) M O(N)

History of FMM 1 J.Barnes, and P.Hut. A Hierarchical O(N log N) Force-Calculation Algorithm. Nature Vol. 324, pp. 446-449, Dec. 4, 1986 2 L.Greengard, and V.Rokhlin. A Fast Algorithm for Particle Simulations. J. Comput. Phys. 73, pp. 325-348, 1987 3 J.Carrier, L.Greengard, and V.Rokhlin. A Fast Adaptive Multipole Algorithm for Particle Simulations. SIAM J. Sci. Stat. Comput. Vol. 9, No. 4, pp. 669-686, July 1988. 4 R.Beatson and L.Greengard. A Short Course on Fast Multipole Methods. Numerical Methematics and Scientific Computation, Wavelets, Multilevel Methods and Elliptic PDEs. Oxford University Press, pp. 1-37, 1997 5 B.Shanker and H.Huang. Accelerated Cartesian Expansions - A Fast Method for Computing of Potentials of the Form R ν for All Real v. J. Comput. Phys. 226, pp. 732-753, 2007

Some concepts Cut box, near region, and far region. First Level Second Level Near region (neighbors) Far region

Some concepts Interaction list and how it works. Interaction list Interaction list Already calculated

Brief introduction of FMM The process of FMM can be described as follows. Select the level of boxes according to the accuracy needed. From the finest level to the coarest level, calculate the multipole expansion of the charges inside each box. For each box in each level, convert the multipole expansions of the boxes in its interaction list into its local expansion. From the coarest to the finest level, translate the local expansion of each box into its child boxes and add it to the local expansion of the child box In the finest level, in each box calculate the potential or field by the local expansion on each particle inside. The potentials or fields of the particles inside the box or in its near region are calculated directly.

Automatic Taylor expansion of a function f (x + δx) = f (x) + f (x)δx + 1 2! f (x)δx 2 + 1 3! f (x)δx 3 +... In Cosy, f (x+da(1)) = f (x)+f (x)da(1)+ 1 2! f (x)da(1) 2 + 1 3! f (x)da(1) 3 +... Composition of two maps G(x) = G(F) F(x), or G(x) = G(F(x)) In COSY, it can be done by the command POLVAL L P NP A NA R NR

Multipole expansion from charges R(x, y, z) R(x, y, z) R 1 R2 φ R ( R i ) = φ R ( M) M S(0, 0, 0) S(0, 0, 0) R i

Multipole expansion from charges (for boxes in the finest level) φ c2m = with N i=1 Q i (xi x) 2 + (y i y) 2 + (z i z) 2 = 1 + x2 i +y2 i +zi 2 x 2 +y 2 +z 2 d 1 = d 3 = Q i / x 2 + y 2 + z 2 2x ix x 2 +y 2 +z 2 2y iy x 2 +y 2 +z 2 2z iz x 2 +y 2 +z 2 Q i d 1 =, 1 + (xi 2 + yi 2 + zi 2)d2 1 2x id 2 2y i d 3 2z i d 4 1 x 2 + y 2 + z 2 = 1 r, d 2 = y x 2 + y 2 + z 2 = y r 2, d 4 = x x 2 + y 2 + z 2 = x r 2, z x 2 + y 2 + z 2 = z r 2,

Multipole expansion in the higher level boxes M M M M M

Translation of a Multipole Expansion R(x, y, z) R(x, y, z ) S (x s, y s, z s) M S (0, 0, 0) M S(0, 0, 0) φ R ( M) = φ R ( M ) S( x s, y s, z s)

DA variables in S frame. d 1 = 1 x 2 + y 2 + z 2 = 1 r, d 2 = d 3 = y x 2 + y 2 + z 2 = y x x x 2 + y 2 = + z 2 r 2, r 2, d 4 = z x 2 + y 2 + z 2 = z Relation between the new and old DA variables (M 1 ). d 1 = 1 + (x s 2 + y s 2 + z s 2 ( d d 2 = 2 d 1 2 + x s d 1 )d 2 ) ( d d1 2, d 3 = 3 d 1 2 + y s 1 + 2x s d 2 + 2y s d 3 + 2z sd 4 Potential in S frame is φ m2m = φ c2m M 1. r 2, ) ( d d1 2, d 4 = 4 d 1 2 + z s ) d 2 1.

Conversion of a Multipole Expansion (in the interaction list) into a Local Expansion R(x, y, z) R(x, y, z ) O(x o, y o, z o ) L O(0, 0, 0) M S(0, 0, 0) φ R ( M) = φ R ( L) S( x o, y o, z o )

DA variables in the observer frame O. d 1 = x, d 2 = y, d 3 = z. Relation between the DA variables in the source frame S and the observer frame O. (M 2 ) d 1 = 1 (xo + d 1 )2 + (y o + d 2 )2 + (z o + d 3 )2 d 2 = d 3 = d 4 = x o + d 1 (x o + d 1 )2 + (y o + d 2 )2 + (z o + d 3 )2, y o + d 2 (x o + d 1 )2 + (y o + d 2 )2 + (z o + d 3 )2, z o + d 3 (x o + d 1 )2 + (y o + d 2 )2 + (z o + d 3 )2. The potential in the observer frame O is φ m2l = φ c2m M 2.

Local Expansion inherited from the parent box. L L L L L

Translation of a Local Expansion R(x, y,z) R(x, y, z ) L L O (x o, y o, z o) O(0, 0,0) O (0,0, 0) φ R ( L) = φ R ( L ) O( x o, y o, z o)

DA variables in the new observer frame O. d 1 = x, d 2 = y, d 3 = z. Relation between the DA variables in the old and new frame. (M 3 ) d 1 = x o + d 1, d 2 = y o + d 2, d 3 = z o + d 3. The potential in the observer frame O is φ l2l = φ m2l M 3.

Field is easy to calculate. In the local expansion, the potential is represented as a polynomial of the local coordinates. The calculate the field, one just need to take derivative of the the respect coordinate. This is also important in the high order map method.

Check the convergence. Case 1, source box center (0,0,0), observer box center (2,0,0), length of the box side 1, 50 electrons in each box, repeat 1000 times. order 3 4 5 6 7 M error 1.488E-3 1.040E-3 1.454E-4 6.067E-5 2.104E-5 L error 3.026E-3 1.264E-3 2.170E-4 8.357E-5 2.655E-5 Case 2, source box center (0,0,0), observer box center (2,2,0), length of the box side 1, 50 electrons in each box, repeat 1000 times. order 3 4 5 6 7 M error 3.923E-4 1.038E-4 2.196E-5 7.561E-6 1.482E-6 L error 7.109E-4 1.568E-4 3.698E-5 9.741E-6 2.075E-6

Case 3, source box center (0,0,0), observer box center (2,2,2), length of the box side 1, 50 electrons in each box, repeat 1000 times. order 3 4 5 6 7 M error 2.567E-4 7.852E-5 8.991E-6 2.315E-6 4.031E-7 L error 4.281E-4 1.088E-4 1.475E-5 3.451E-6 6.133E-7 Case 4, DA order 3, 50 electrons in each box, repeat 1000 times, change the position of the observer box,50 electrons in each box, repeat 1000 times. observer (2,0,0) (3,0,0) (4,0,0) (5,0,0) M error 1.488E-3 3.361E-4 1.248E-4 5.966E-5 L error 3.026E-3 6.308E-4 2.116E-4 9.265E-5

Work to do Finish the code,some low level tools for COSY to increase efficiency. How about bunch size in some dimension is much larger/smaller than the other dimensions. Apply it in some simulation. Error Analysis, rigorous error bound by TM.

Thank you!