Finite difference methods An introduction. Jean Virieux Professeur UJF with the help of Virginie Durand
|
|
- Silas Carpenter
- 5 years ago
- Views:
Transcription
1 Fte dfferece methods A trodcto Jea Vre Professer JF wth the help of Vrge Drad
2 A global vso Dfferetal Calcls (Newto, 1687 & Lebz 1684) Fd soltos of a dfferetal eqato (DE) of a dyamc system. Chaos Systems (Pocaré, 1881) Fd propertes of soltos of the DE of a dyamc system. Chaos & Stablty (Smale, 1960) Fd propertes of soltos of a physcal system wthot kowg ts DE After the presetato of Etee Ghys, 13 october 009 Ths corse s abot the dfferetal calcls sg the fte dfferece approach famlar to Newto & Lebz
3 Bblography o Fte Dfferece Methods : A. Taflove ad S. C. Hagess: Comptatoal Electrodyamcs: The Fte- Dfferece Tme-Doma Method, Thrd Edto, Artech Hose Pblshers, 005 O.C. Zekewcz ad K. Morga: Fte elemets ad apprommato, Wley, New York, 198 W.H. Press et al, Nmercal recpes FORTRAN/C Cambrdge versty Press, SA, 0XX Spce grop Erope : FDTD trodcto : ftp://ftp.sesmology.sk/pb/papers/fdm-itro-spice.pdf By P. Moczo, J. Krstek ad L. Halada
4 What s the ma dea? Coverage of the comptatoal doma by a space-tme grd Appromate dervatves ad tal vales at all grd pots Defe bodary codtos at ed pots Costrct the lear algebrac system to be solved by the compter
5 The heat eqato t t (, ) t (, ) k k s the thermal dffso coeffcet (, t) Grate Basalte Calcare Ea Glace Sol sec Sol hmde (8%) k e m /s 1, , , , , , k Replace partal dervatves by fte dfferece appromatos leadg to a algebrac system (,t) ~ where the de s for the dscrete spatal posto ad for the dscrete tme level
6 Other PDE physcs The scalar wave eqato s a partal dfferetal eqato whch belogs to secod-order hyperbolc system. Tme s volved all physcal processes ecept for the Laplace eqato related to Newto law ad mass dstrbto. Posso eqato cold be cosdered as well whe mass s dstrbted sde the vestgated volme t (, ) t (, ) a (, t) t Wave Eqato Fld Eqato Dffso Eqato Laplace Eqato Fractoal dervatve Eqato t (, ) t (, ) c ( ) t t (, ) t (, ) ( ) t t t (, ) t (, ) ( ) 0 t t t (, ) (, ) t (, ) ( ) Advecto Eqato Posso Eqato f( ) ( )
7 Fte Dfferece Stecl (Leveqe 199) cetered h D backward h D forward h D Trcatos errors : 0 h Secod dervatve D D D D D 0 0 ) ( h D Hgher-order terms : same procedre bt yo eed more ad more pots h
8 Fte dfferece appromato D 1 h forward D h 1 backward Forward/backward frst-order appromato D o 1 1 h cetered Cetered secod-order appromato 1 D ( D D ) o Hgher-order appromatos cold be cosdered as well : more vales!!!
9 Dalty betwee a fcto ad ts dervatve Secod-order accrate cetral-dfferece appromato Leapfrog secod-order accrate cetral-dfferece appromato Leapfrog 4 th -order accrate cetral-dfferece appromato Stecl legth
10 4 4 4, 3 3 3,,, 1, 4 6 ) ( 4 4 4, 3 3 3,,, 1, 4 6 ) ( 4 4 4,, 1, 1, 1 Dscretsato ad Taylor epaso ) (, 1, 1,, Assmg a form dscretsato,t o the doma, we cosder terpolato pto power 4 by smmg, we cacel ot odd terms eglectg power 4 terms of the dscretsato steps. We are left wth qadratc terpolatos, althogh cbc terms cacel ot for precso.
11 Secod dervatves Varos methods for evalato of secod dervatves 11 D h 1 D D D D D h h h 1 1 Taylor terpolato formlato related to Lagrade polyomals ad, therefore, vales are ot restrcted to odes of a grd we assme a smooth cotrbto of the mercal solto. The solto of the heat eqato has a mercal appromate solto 1 1 t t 11
12
13 Is the mercal solto correct? Stablty: Errors are boded drg the comptato of the solto. Cosstece: Trcato errors go to zero whe dscretsato gets smaller. Covergece: Nmercal soltos go to the eact solto as dscretsato gets smaller. Solto Accracy
14 Heterogeeos meda Whe cosderg mercal methods, we ofte address the problem of comple meda for whch there s o aalytcal solto,, Spatal dervatves of medm propertes shold be estmated,,,, Decreasg the order of dervatves by addg alary varables whch have ofte physcal meags.
15 Parsmoos approach F RC V K t 1 1 V V Stadard grd 1 1 K 1 K 1 F RC RC t K K ( K K ) t Not eactly waht we wat becase of dces + ad -: we eed to move to a med grd approach (dalty betwee a fcto ad ts dervatve) F
16 Parsmoos approach 1 1 1/ 1/ F RC V K t 1/ 1/ 1 V V Staggered grd or med grd approach K 1/ K 1/ F RC t K K ( K K ) 1 1 1/ 1 1/ 1 1/ 1/ F RC t Same dces tha the homogeeos case: arthmetc average comg from the physcs behd Idetfy how to defe jmps at a terface
17 Tme marchg procedre Eplct cetered scheme t IS THAT ALL? A very smple costrcto: estmato of a ftre vale from preset vales ad oe past vale. Ital vales: Bodary vales: Drchlet codtos & L 0
18 Freqecy approach Fte Dfferece formlato leads to a lear system K K ( K K ) 1/ 1 1/ 1 1/ 1/ F RC WRITE ON THE BLACKBOARD THE LINEAR SYSTEM
19 FIND A SOLTION? I Tme : marchg approach I Freqecy : lear algebra
20 ODE verss PDE formlatos d yt () Ayt ( (,), t,) dt d yt () At (,) yt (,) dt yt (, ) t yt (, ) t No-lear Dyt ( (, ), t, ) D( t, ) y( t, ) Lear O.D.E Ordary dfferetal Eqatos P.D.E Partal Dfferetal Eqatos Symmetry betwee space ad tme? GOAL : fd ways to trasform dfferetal operators to algebrac operators order to se lear algebra at the ed «smple» solto «comple» solto
21 EXISTENCE & NIQENESS The mercal problem s well posed sg Hadamard crtera f the solto ests, s qe ad depeds cotosly to tal/bodary codtos, to bodares ad coeffcets of the dfferetal eqatos Vo Nema stablty aalyss for FD methods: mercal tegrato caot go beyod physcal dstaces of teracto. For eample, Corat-Fredrchs-Lewy CFL codto for wave propagato
22 Frst-order hyperbolc eqato E v t v t c t v E t Let s defe other varables for redcg the dervatve order both tme ad space The d order PDE became a 1st order PDE Ths s tre for ay order dfferetal eqatos: by trodcg addtoal varables, oe ca redce the level of dfferetato. Amog these dfferet systems, oe has a physcal meag whch becomes v E t t v 1 E c wth stress velocty Other choces are possble as dsplacemet-stress stead of velocty-stress.
23
24 Ital ad bodary codtos 1D strg medm Ital codtos (,0) Bodary codtos (0,t) Bodary codtos (L,t) Drchlet codtos o Nema codtos o f(,t)=s(t)r() Ectato codto,, Dffclt to see how to dscretze the velocty c()! Mch better for hadlg heterogeetes,,,,
25
26
27
28 v=0
29 Sorce ectato Implsve sorce: Rcker wavelet for eample 1 Oscllatory sorce: Ss wavelet for eample s
30 Sorce radato Drectoal sorce (hammer) Eplosve sorce (dyamte) Meag for the strg? Applcato of of opposte sg forces o two odes or a fctos force betwee two odes
31
32
33
34
35 Smlato a two-layer medm c1=000 m/s c=4000 m/s - S=1 the hgh-velocty layer Radato bodary codto Code df1d.3.f
36 Sesmc propagato the Agel Bay earby Nce (Frace) Earthqake of magtde 4.9 at a depth of 8 km
37 CONCLSION Effcet mercal methods for propagatg sesmc waves Tme tegrato verss freqecy tegrato Competto betwee FE & FV for modellg FD a effcet tool for magg
38 THANKS YO!
u(x, t) = u 0 (x ct). This Riemann invariant u is constant along characteristics λ with x = x 0 +ct (u(x, t) = u 0 (x 0 )):
x, t, h x The Frst-Order Wave Eqato The frst-order wave advecto eqato s c > 0 t + c x = 0, x, t = 0 = 0x. The solto propagates the tal data 0 to the rght wth speed c: x, t = 0 x ct. Ths Rema varat s costat
More informationu(x, t) = u 0 (x ct). This Riemann invariant u is constant along characteristics λ with x = x 0 +ct (u(x, t) = u 0 (x 0 )):
x, t ), h x The Frst-Order Wave Eqato The frst-order wave advecto) eqato s c > 0) t + c x = 0, x, t = 0) = 0x). The solto propagates the tal data 0 to the rght wth speed c: x, t) = 0 x ct). Ths Rema varat
More informationDiscretization Methods in Fluid Dynamics
Corse : Fld Mechacs ad Eergy Coverso Dscretzato Methods Fld Dyamcs Mayak Behl B-tech. 3 rd Year Departmet of Chemcal Egeerg Ida Isttte of Techology Delh Spervsor: Dr. G.Bswas Ida Isttte of Techology Kapr
More informationAn Expansion of the Derivation of the Spline Smoothing Theory Alan Kaylor Cline
A Epaso of the Derato of the Sple Smoothg heory Ala Kaylor Cle he classc paper "Smoothg by Sple Fctos", Nmersche Mathematk 0, 77-83 967) by Chrsta Resch showed that atral cbc sples were the soltos to a
More informationLecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES
FDM: Appromato of Frst Order Dervatves Lecture APPROXIMATION OF FIRST ORDER DERIVATIVES. INTRODUCTION Covectve term coservato equatos volve frst order dervatves. The smplest possble approach for dscretzato
More informationPGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation
PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad
More informationDiscrete Adomian Decomposition Method for. Solving Burger s-huxley Equation
It. J. Cotemp. Math. Sceces, Vol. 8, 03, o. 3, 63-63 HIKARI Ltd, www.m-har.com http://dx.do.org/0.988/jcms.03.3570 Dscrete Adoma Decomposto Method for Solvg Brger s-hxley Eqato Abdlghafor M. Al-Rozbaya
More informationThe Finite Volume Method for Solving Systems. of Non-linear Initial-Boundary. Value Problems for PDE's
Appled Matematcal Sceces, Vol. 7, 13, o. 35, 1737-1755 HIKARI Ltd, www.m-ar.com Te Fte Volme Metod for Solvg Systems of No-lear Ital-Bodary Vale Problems for PDE's 1 Ema Al Hssa ad Zaab Moammed Alwa 1
More informationNumericalSimulationofWaveEquation
Global Joral of Scece Froter Research: A Physcs ad Space Scece Volme 4 Isse 7 Verso. Year 4 Type : Doble Bld Peer Revewed Iteratoal Research Joral Pblsher: Global Jorals Ic. (USA Ole ISSN: 49-466 & Prt
More informationB-spline curves. 1. Properties of the B-spline curve. control of the curve shape as opposed to global control by using a special set of blending
B-sple crve Copyrght@, YZU Optmal Desg Laboratory. All rghts reserved. Last pdated: Yeh-Lag Hs (--9). ote: Ths s the corse materal for ME Geometrc modelg ad compter graphcs, Ya Ze Uversty. art of ths materal
More informationBeam Warming Second-Order Upwind Method
Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet
More informationDISTURBANCE TERMS. is a scalar and x i
DISTURBANCE TERMS I a feld of research desg, we ofte have the qesto abot whether there s a relatoshp betwee a observed varable (sa, ) ad the other observed varables (sa, x ). To aswer the qesto, we ma
More information828. Piecewise exact solution of nonlinear momentum conservation equation with unconditional stability for time increment
88. Pecewse exact solto of olear mometm coservato eqato wth codtoal stablty for tme cremet Chaghwa Jag, Hyoseob Km, Sokhwa Cho 3, Jho Km 4 Korea Itellectal Property Offce, Daejeo, Korea, 3 Kookm Uversty,
More informationInternational Journal of Scientific & Engineering Research, Volume 5, Issue 9, September ISSN
Iteratoal Joral o Scetc & Egeerg Research, Volme 5, Isse 9, September-4 5 ISSN 9-558 Nmercal Implemetato o BD va Method o Les or Tme Depedet Nolear Brgers Eqato VjthaMkda, Ashsh Awasth Departmet o Mathematcs,
More informationLecture 2: The Simple Regression Model
Lectre Notes o Advaced coometrcs Lectre : The Smple Regresso Model Takash Yamao Fall Semester 5 I ths lectre we revew the smple bvarate lear regresso model. We focs o statstcal assmptos to obta based estmators.
More informationFractional Order Finite Difference Scheme For Soil Moisture Diffusion Equation And Its Applications
IOS Joural of Mathematcs (IOS-JM e-iss: 78-578. Volume 5, Issue 4 (Ja. - Feb. 3, PP -8 www.osrourals.org Fractoal Order Fte Dfferece Scheme For Sol Mosture Dffuso quato Ad Its Applcatos S.M.Jogdad, K.C.Takale,
More information( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix.
Revew + v, + y = v, + v, + y, + y, Cato! v, + y, + v, + y geeral Let A be a atr Let f,g : Ω R ( ) ( ) R y R Ω R h( ) f ( ) g ( ) ( ) ( ) ( ( )) ( ) dh = f dg + g df A, y y A Ay = = r= c= =, : Ω R he Proof
More informationAn Alternative Strategy for the Solution of Heat and Incompressible Fluid Flow Problems via Finite Volume Method
A Alteratve Strategy for the Solto of Heat ad Icompressble Fld Flow Problems va Fte Volme Method Masod Nckaee a, Al Ashrafzadeh b, Stefa Trek a a Isttte of Appled Mathematcs, Dortmd Uversty of Techology,
More informationMeromorphic Solutions of Nonlinear Difference Equations
Mathematcal Comptato Je 014 Volme 3 Isse PP.49-54 Meromorphc Soltos of Nolear Dfferece Eatos Xogyg L # Bh Wag College of Ecoomcs Ja Uversty Gagzho Gagdog 51063 P.R.Cha #Emal: lxogyg818@163.com Abstract
More information2.160 System Identification, Estimation, and Learning Lecture Notes No. 17 April 24, 2006
.6 System Idetfcato, Estmato, ad Learg Lectre Notes No. 7 Aprl 4, 6. Iformatve Expermets. Persstece of Exctato Iformatve data sets are closely related to Persstece of Exctato, a mportat cocept sed adaptve
More informationAn Improved CIP-CUP Method for Submerged Water Jet Flow Simulation
Y 1àÁ oyõgfp+s D05-1 Óoo+6;yéRX_oZfæ Ô wÿcip-cup:ãøé= A Improved CIP-CUP Method for Sbmerged Water Jet Flow Smlato Ä áõ, õ", õêö %! 5180, E-mal: peg@p-toyama.ac.p y N, õ", õêö %! 5180, E-mal: shzka@p-toyama.ac.p
More informationCS475 Parallel Programming
CS475 Parallel Programmg Deretato ad Itegrato Wm Bohm Colorado State Uversty Ecept as otherwse oted, the cotet o ths presetato s lcesed uder the Creatve Commos Attrbuto.5 lcese. Pheomea Physcs: heat, low,
More informationAlternating Direction Implicit Method
Alteratg Drecto Implct Method Whle dealg wth Ellptc Eqatos the Implct form the mber of eqatos to be solved are N M whch are qte large mber. Thogh the coeffcet matrx has may zeros bt t s ot a baded system.
More informationLecture 5: Interpolation. Polynomial interpolation Rational approximation
Lecture 5: Iterpolato olyomal terpolato Ratoal appromato Coeffcets of the polyomal Iterpolato: Sometme we kow the values of a fucto f for a fte set of pots. Yet we wat to evaluate f for other values perhaps
More informationMotion Estimation Based on Unit Quaternion Decomposition of the Rotation Matrix
Moto Estmato Based o Ut Qatero Decomposto of the Rotato Matrx Hag Y Ya Baozog (Isttte of Iformato Scece orther Jaotog Uversty Bejg 00044 PR Cha Abstract Based o the t qatero decomposto of rotato matrx
More informationLecture Notes Types of economic variables
Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte
More informationThis article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and
Ths artcle appeared a joral pblshed by Elsever. The attached copy s frshed to the athor for teral o-commercal research ad edcato se, cldg for strcto at the athors sttto ad sharg wth colleages. Other ses,
More informationDKA method for single variable holomorphic functions
DKA method for sgle varable holomorphc fuctos TOSHIAKI ITOH Itegrated Arts ad Natural Sceces The Uversty of Toushma -, Mamhosama, Toushma, 770-8502 JAPAN Abstract: - Durad-Kerer-Aberth (DKA method for
More informationFundamentals of Regression Analysis
Fdametals of Regresso Aalyss Regresso aalyss s cocered wth the stdy of the depedece of oe varable, the depedet varable, o oe or more other varables, the explaatory varables, wth a vew of estmatg ad/or
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationInverse Problem of Finding an Unknown Parameter for One- and Two-dimensional Parabolic Heat Equations
Iverse Problem of Fdg a Ukow Parameter for Oe- ad Two-dmesoal Parabolc Heat Eqatos Mohamed Elmadob Problem Report sbmtted to the Statler College of Egeerg ad Meral Resorces at West Vrga Uversty partal
More information1 Lyapunov Stability Theory
Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may
More informationCS5620 Intro to Computer Graphics
CS56 Itro to Computer Graphcs Geometrc Modelg art II Geometrc Modelg II hyscal Sples Curve desg pre-computers Cubc Sples Stadard sple put set of pots { } =, No dervatves specfed as put Iterpolate by cubc
More informationTaylor s Series and Interpolation. Interpolation & Curve-fitting. CIS Interpolation. Basic Scenario. Taylor Series interpolates at a specific
CIS 54 - Iterpolato Roger Crawfs Basc Scearo We are able to prod some fucto, but do ot kow what t really s. Ths gves us a lst of data pots: [x,f ] f(x) f f + x x + August 2, 25 OSU/CIS 54 3 Taylor s Seres
More informationEvolution Operators and Boundary Conditions for Propagation and Reflection Methods
voluto Operators ad for Propagato ad Reflecto Methods Davd Yevck Departmet of Physcs Uversty of Waterloo Physcs 5/3/9 Collaborators Frak Schmdt ZIB Tlma Frese ZIB Uversty of Waterloo] atem l-refae Nortel
More information1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers.
PROBLEMS A real umber s represeted appromately by 63, ad we are told that the relatve error s % What s? Note: There are two aswers Ht : Recall that % relatve error s What s the relatve error volved roudg
More informationNumerical Analysis Formulae Booklet
Numercal Aalyss Formulae Booklet. Iteratve Scemes for Systems of Lear Algebrac Equatos:.... Taylor Seres... 3. Fte Dfferece Approxmatos... 3 4. Egevalues ad Egevectors of Matrces.... 3 5. Vector ad Matrx
More informationMean is only appropriate for interval or ratio scales, not ordinal or nominal.
Mea Same as ordary average Sum all the data values ad dvde by the sample sze. x = ( x + x +... + x Usg summato otato, we wrte ths as x = x = x = = ) x Mea s oly approprate for terval or rato scales, ot
More informationLecture Notes Forecasting the process of estimating or predicting unknown situations
Lecture Notes. Ecoomc Forecastg. Forecastg the process of estmatg or predctg ukow stuatos Eample usuall ecoomsts predct future ecoomc varables Forecastg apples to a varet of data () tme seres data predctg
More informationUNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS
Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method
More informationGeneralized Linear Models. Statistical Models. Classical Linear Regression Why easy formulation if complicated formulation exists?
Statstcal Models Geeralzed Lear Models Classcal lear regresso complcated formlato of smple model, strctral ad radom compoet of the model Lectre 5 Geeralzed Lear Models Geeralzed lear models geeral descrpto
More informationThe First Order Saddlepoint Approximation for. Reliability Analysis
AIAA Joral, Vol. 4, No. 6, 004, pp. 99-07 The Frst Order Saddlepot Approxmato for Relablty Aalyss Xaopg D Uversty of Mssor Rolla, Rolla, MO 65409-0050 Ags Sdjato + Ford Motor Compay, Dearbor, MI 48-409
More informationA FINITE DIFFERENCE SCHEME FOR A FLUID DYNAMIC TRAFFIC FLOW MODEL APPENDED WITH TWO-POINT BOUNDARY CONDITION
GANIT J. Bagladesh Math. Soc. (ISSN 66-3694 3 ( 43-5 A FINITE DIFFERENCE SCHEME FOR A FLUID DYNAMIC TRAFFIC FLOW MODEL APPENDED WITH TWO-POINT BOUNDARY CONDITION M. O. Ga, M. M. Hossa ad L. S. Adallah
More informationMOLECULAR VIBRATIONS
MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal
More informationbest estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best
Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg
More informationESS Line Fitting
ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here
More informationENGI 4421 Propagation of Error Page 8-01
ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.
More informationConvergence of the Desroziers scheme and its relation to the lag innovation diagnostic
Covergece of the Desrozers scheme ad ts relato to the lag ovato dagostc chard Méard Evromet Caada, Ar Qualty esearch Dvso World Weather Ope Scece Coferece Motreal, August 9, 04 o t t O x x x y x y Oservato
More informationModule 7. Lecture 7: Statistical parameter estimation
Lecture 7: Statstcal parameter estmato Parameter Estmato Methods of Parameter Estmato 1) Method of Matchg Pots ) Method of Momets 3) Mamum Lkelhood method Populato Parameter Sample Parameter Ubased estmato
More informationCubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem
Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs
More information( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006
Mult-Layer Refracto Problem Rafael Espercueta, Bakersfeld College, November, 006 Lght travels at dfferet speeds through dfferet meda, but refracts at layer boudares order to traverse the least-tme path.
More informationChapter 5 Properties of a Random Sample
Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample
More informationSampling Theory MODULE V LECTURE - 14 RATIO AND PRODUCT METHODS OF ESTIMATION
Samplg Theor MODULE V LECTUE - 4 ATIO AND PODUCT METHODS OF ESTIMATION D. SHALABH DEPATMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOG KANPU A mportat objectve a statstcal estmato procedure
More information1 0, x? x x. 1 Root finding. 1.1 Introduction. Solve[x^2-1 0,x] {{x -1},{x 1}} Plot[x^2-1,{x,-2,2}] 3
Adrew Powuk - http://www.powuk.com- Math 49 (Numercal Aalyss) Root fdg. Itroducto f ( ),?,? Solve[^-,] {{-},{}} Plot[^-,{,-,}] Cubc equato https://e.wkpeda.org/wk/cubc_fucto Quartc equato https://e.wkpeda.org/wk/quartc_fucto
More informationEnd of Finite Volume Methods Cartesian grids. Solution of the Navier-Stokes Equations. REVIEW Lecture 17: Higher order (interpolation) schemes
REVIEW Lecture 17: Numercal Flud Mechacs Sprg 2015 Lecture 18 Ed of Fte Volume Methods Cartesa grds Hgher order (terpolato) schemes Soluto of the Naver-Stokes Equatos Dscretzato of the covectve ad vscous
More informationEECE 301 Signals & Systems
EECE 01 Sgals & Systems Prof. Mark Fowler Note Set #9 Computg D-T Covoluto Readg Assgmet: Secto. of Kame ad Heck 1/ Course Flow Dagram The arrows here show coceptual flow betwee deas. Note the parallel
More informationd dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin
Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace
More information4 Round-Off and Truncation Errors
HK Km Slgtly moded 3//9, /8/6 Frstly wrtte at Marc 5 4 Roud-O ad Trucato Errors Errors Roud-o Errors Trucato Errors Total Numercal Errors Bluders, Model Errors, ad Data Ucertaty Recallg, dv dt Δv v t Δt
More informationStatistics Descriptive and Inferential Statistics. Instructor: Daisuke Nagakura
Statstcs Descrptve ad Iferetal Statstcs Istructor: Dasuke Nagakura (agakura@z7.keo.jp) 1 Today s topc Today, I talk about two categores of statstcal aalyses, descrptve statstcs ad feretal statstcs, ad
More informationMu Sequences/Series Solutions National Convention 2014
Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed
More informationSome Applications of the Resampling Methods in Computational Physics
Iteratoal Joural of Mathematcs Treds ad Techoloy Volume 6 February 04 Some Applcatos of the Resampl Methods Computatoal Physcs Sotraq Marko #, Lorec Ekoom * # Physcs Departmet, Uversty of Korca, Albaa,
More informationSummary of the lecture in Biostatistics
Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the
More informationFourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION
Malasa Joural of Mathematcal Sceces (): 95-05 (00) Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method for Lear Ordar Dfferetal Equatos Nur Izzat Che Jawas, Fudzah Ismal, Mohamed Sulema, 3 Azm Jaafar
More informationSimple Linear Regression
Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato
More informationInvestigation of Partially Conditional RP Model with Response Error. Ed Stanek
Partally Codtoal Radom Permutato Model 7- vestgato of Partally Codtoal RP Model wth Respose Error TRODUCTO Ed Staek We explore the predctor that wll result a smple radom sample wth respose error whe a
More informationDerivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations
Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat
More informationX ε ) = 0, or equivalently, lim
Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece
More informationInstituto Tecnológico de Aeronáutica FINITE ELEMENTS I. Class notes AE-245
Isttto Tecológco de Aeroátca FIITE ELEETS I Class otes AE-45 Isttto Tecológco de Aeroátca 8. Beams ad Plates AE-45 Isttto Tecológco de Aeroátca BEAS AD PLATES Itrodcto Eler-Beroll beam model ad Krcoff
More informationMultiple Choice Test. Chapter Adequacy of Models for Regression
Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to
More informationECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013
ECE 595, Secto 0 Numercal Smulatos Lecture 9: FEM for Electroc Trasport Prof. Peter Bermel February, 03 Outle Recap from Wedesday Physcs-based devce modelg Electroc trasport theory FEM electroc trasport
More informationL5 Polynomial / Spline Curves
L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a
More informationInvestigating Cellular Automata
Researcher: Taylor Dupuy Advsor: Aaro Wootto Semester: Fall 4 Ivestgatg Cellular Automata A Overvew of Cellular Automata: Cellular Automata are smple computer programs that geerate rows of black ad whte
More informationSTK4011 and STK9011 Autumn 2016
STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto
More informationLine Fitting and Regression
Marquette Uverst MSCS6 Le Fttg ad Regresso Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 8 b Marquette Uverst Least Squares Regresso MSCS6 For LSR we have pots
More informationG S Power Flow Solution
G S Power Flow Soluto P Q I y y * 0 1, Y y Y 0 y Y Y 1, P Q ( k) ( k) * ( k 1) 1, Y Y PQ buses * 1 P Q Y ( k1) *( k) ( k) Q Im[ Y ] 1 P buses & Slack bus ( k 1) *( k) ( k) Y 1 P Re[ ] Slack bus 17 Calculato
More informationAnalysis of Lagrange Interpolation Formula
P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal
More informationLecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model
Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The
More informationSTATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ
More informationUnsupervised Learning and Other Neural Networks
CSE 53 Soft Computg NOT PART OF THE FINAL Usupervsed Learg ad Other Neural Networs Itroducto Mture Destes ad Idetfablty ML Estmates Applcato to Normal Mtures Other Neural Networs Itroducto Prevously, all
More informationCSE 5526: Introduction to Neural Networks Linear Regression
CSE 556: Itroducto to Neural Netorks Lear Regresso Part II 1 Problem statemet Part II Problem statemet Part II 3 Lear regresso th oe varable Gve a set of N pars of data , appromate d by a lear fucto
More informationLECTURE - 4 SIMPLE RANDOM SAMPLING DR. SHALABH DEPARTMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOGY KANPUR
amplg Theory MODULE II LECTURE - 4 IMPLE RADOM AMPLIG DR. HALABH DEPARTMET OF MATHEMATIC AD TATITIC IDIA ITITUTE OF TECHOLOGY KAPUR Estmato of populato mea ad populato varace Oe of the ma objectves after
More informationN-dimensional Auto-Bäcklund Transformation and Exact Solutions to n-dimensional Burgers System
N-dmesoal Ato-Bäckld Trasformato ad Eact Soltos to -dmesoal Brgers System Mglag Wag Jlag Zhag * & Xagzheg L. School of Mathematcs & Statstcs Hea Uversty of Scece & Techology Loyag 4703 PR Cha. School of
More informationto the estimation of total sensitivity indices
Applcato of the cotrol o varate ate techque to the estmato of total sestvty dces S KUCHERENKO B DELPUECH Imperal College Lodo (UK) skuchereko@mperalacuk B IOOSS Electrcté de Frace (Frace) S TARANTOLA Jot
More information( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model
Chapter 3 Asmptotc Theor ad Stochastc Regressors The ature of eplaator varable s assumed to be o-stochastc or fed repeated samples a regresso aalss Such a assumpto s approprate for those epermets whch
More informationLaboratory I.10 It All Adds Up
Laboratory I. It All Adds Up Goals The studet wll work wth Rema sums ad evaluate them usg Derve. The studet wll see applcatos of tegrals as accumulatos of chages. The studet wll revew curve fttg sklls.
More information5 Short Proofs of Simplified Stirling s Approximation
5 Short Proofs of Smplfed Strlg s Approxmato Ofr Gorodetsky, drtymaths.wordpress.com Jue, 20 0 Itroducto Strlg s approxmato s the followg (somewhat surprsg) approxmato of the factoral,, usg elemetary fuctos:
More informationECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity
ECONOMETRIC THEORY MODULE VIII Lecture - 6 Heteroskedastcty Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur . Breusch Paga test Ths test ca be appled whe the replcated data
More informationCHAPTER VI Statistical Analysis of Experimental Data
Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca
More informationSuggested Answers, Problem Set 4 ECON The R 2 for the unrestricted model is by definition u u u u
Da Hgerma Fall 9 Sggested Aswers, Problem Set 4 ECON 333 The F-test s defed as ( SSEr The R for the restrcted model s by defto SSE / ( k ) R ( SSE / SST ) so therefore, SSE SST ( R ) ad lkewse SSEr SST
More informationENGI 4430 Numerical Integration Page 5-01
ENGI 443 Numercal Itegrato Page 5-5. Numercal Itegrato I some o our prevous work, (most otaly the evaluato o arc legth), t has ee dcult or mpossle to d the dete tegral. Varous symolc algera ad calculus
More informationAnalysis of Variance with Weibull Data
Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad
More informationTOWARDS Non-Commutative (=NC) integrable systems and soliton theories. Masashi HAMANAKA (Nagoya University, Dept. of Math. )
TOWARDS No-Commtatve NC tegrable systems ad solto theores Masash HAMANAKA Nagoya Uversty Dept. o Math. Based o Math. Phys. Semar York o Oct th MH JMP6 005 0570 [hep-th/006] MH PB65 005 [hep-th/0507] c.
More informationLikelihood Ratio, Wald, and Lagrange Multiplier (Score) Tests. Soccer Goals in European Premier Leagues
Lkelhood Rato, Wald, ad Lagrage Multpler (Score) Tests Soccer Goals Europea Premer Leagues - 4 Statstcal Testg Prcples Goal: Test a Hpothess cocerg parameter value(s) a larger populato (or ature), based
More informationOutline. Point Pattern Analysis Part I. Revisit IRP/CSR
Pot Patter Aalyss Part I Outle Revst IRP/CSR, frst- ad secod order effects What s pot patter aalyss (PPA)? Desty-based pot patter measures Dstace-based pot patter measures Revst IRP/CSR Equal probablty:
More informationDescriptive Statistics
Page Techcal Math II Descrptve Statstcs Descrptve Statstcs Descrptve statstcs s the body of methods used to represet ad summarze sets of data. A descrpto of how a set of measuremets (for eample, people
More informationQ-analogue of a Linear Transformation Preserving Log-concavity
Iteratoal Joural of Algebra, Vol. 1, 2007, o. 2, 87-94 Q-aalogue of a Lear Trasformato Preservg Log-cocavty Daozhog Luo Departmet of Mathematcs, Huaqao Uversty Quazhou, Fua 362021, P. R. Cha ldzblue@163.com
More informationMATH 247/Winter Notes on the adjoint and on normal operators.
MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say
More informationTowards developing a reacting- DNS code Design and numerical issues
Towards developg a reactg- DNS code Desg ad umercal ssues Rx Yu 20-03-09 20-03-09 FM teral semar Motvatos Why we eed a Reactg DNS code No model for both flow (turbulece) ad combusto Study applcatos of
More informationC-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory
ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?
More information1 Onto functions and bijections Applications to Counting
1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of
More information