FROM THE BCS EQUATIONS TO THE ANISOTROPIC SUPERCONDUCTIVITY EQUATIONS. Luis A. PérezP. Chumin Wang

Size: px
Start display at page:

Download "FROM THE BCS EQUATIONS TO THE ANISOTROPIC SUPERCONDUCTIVITY EQUATIONS. Luis A. PérezP. Chumin Wang"

Transcription

1 FROM THE BCS EQUATIONS TO THE ANISOTROPIC SUPERCONDUCTIVITY EQUATIONS J. Samuel Mllá Faulad de Igeería Uversdad Auóoma del Carme Méxo. M Lus A. PérezP Isuo de Físa F UNAM MéxoM xo. Chum Wag Isuo de Ivesgaoes e Maerales UNAM Méxo. M

2 Oule MOTIVATION. THE GENERALIZED HUBBARD MODEL. TWO PARTICLES. THE BCS GENERALIZED EQUATIONS. THE p AND d COUPLED EQUATIONS. SUPERCONDUCTING PROPERTIES. CONCLUSIONS.

3 D Superodug Gap Smmer Exeded s* Smmer gs* os xa os g s* s s* s* a d x - Smmer gd os xa os g d d d a p Smmer g p 3 p s xa s g p p a

4 The Par Smmer Sgle : Φ S where Ψ Ψ S r S r r r Ψ S r α β β α r wh α β. Trple 3 : α α ΦT ΨT r r β β where Ψ r r Ψ r r. T T [ α β β α ]

5 The Crsal Sruure Ru-O O plaes Cu-O O plaes Smlar Sruure for Sr-Ru ad La-Ba BaSr ssems

6 The Dsoro of he Square Lae Y 3 Y 3 δ δ 3 δ δ 3 X X The hoppg o frs ad seods eghbors are he same boh dreos ' ' 0 ' 3 3 δ δ The hoppg o seods eghbors are dffere XY XY dreos 3

7 The Expermeal Evdee for a Dsoro o he Surfae Image of he surfae for Sr RuO 4 seeg from up of Ru-O plaes where we a see a dsoro of he oahedros formed b oxges. Mazdorf e al. See

8 The The Hubbard Model Hubbard Model Real Spae Real Spae l l l V U H ˆ > >< >>< << > < > < >> << > < l v r' rd d vl R r R r r' r' r' r' r R r R r * * ϕ ϕ ϕ ϕ ev v ev v ev v V ev v U The egrals The egrals of of erao erao bewee bewee wo wo eleros eleros are: are: * 3 u m r d h R r r R r ϕ ϕ h where where

9 Mapg Mehod o Spae of Saes β β β β x mp 0 0 ' os K os K os a / a / [ K K a / ] ' os K K 3 parg odo: x x [ a / ] x E E > 0 A proeed square lae for 74 wo parles saes wh rple sp orrespodg o spae of saes for hperube of dmeso four. S Mllá e al. Phsa C

10 The Phase Dagram wo parles a Two eleros b Two holes boh wh U6 0 V0 D0.5 0 D

11 The Elero Levels a d p s a p s d b d p s p b p s p d Two eleros Two holes

12 The The Geeralzed Hubbard Model Geeralzed Hubbard Model l l l V U H ˆ > > < >> < << > < > < >> << > < ξ ' ' ' ' ' ' ' ' ˆ W N V N H s s [ ] [ ] [ ] ' ' ' ' ' 3 3 ' ς γ β β β β β V U V ' ' ' 3 3 ' ς γ β V W [ ] [ ] [ ] a a ' a ' a a a ' a ' a a a x x x x x os os os os os os ' ' ' ' ς γ β Real Real Spae Spae: Reproal Spae Reproal Spae: Poeal Ierao wh Aparallel Sp Poeal Ierao wh Aparallel Sp: Poeal Ierao wh Parallel Sp Poeal Ierao wh Parallel Sp: where where [ ] µ ε ξ 0

13 BCS Geeralzed Theor ˆ - - H ε 0 µ V ' ' ' ' ' ' For he sgle ase he spaal par of he eerge gap s ' ' ' f η ' V e ξ η gη [ where η s s* d gs* s / s* os xa os a g os a os a whle he rple ase d 3 p V ' o ' ' wh η p g [ s a s a ] η ] [ ] [ ] x x ξ ' f η g ' η ' ' ' 3 p ' Mlla e al. Phss Leers A

14 p-smmer where d-smmer where The Coupled Equao for p ad d Smmer V 4δ 3 s xa s as x Ns E p E p ε0 µ p s xa s a N a [ ] [ ] V 4 3 Ns E d µ he Chemal Poeal µ for he Superoduor Sae sasfed 0 s ε µ Eη ah E T η os a os os x a E ah ad wh he mea feld aproxmao: U ε 4V 0 x 3 x 3 E p T [ ] ' ' os a os a os os 0 x x [ ε ] [ os a os ] 0 d x a B d µ B a E ah d T B D ε dε

15 T vs for d ad p Smmeres B T / B T / / Ssems wh UV δ I. Maz e al. Phs. Rev. Le δ squares rle rles up ragl gles s 0. 0 dow ragl gles rhomb ombus. Ise:0.6 bla 0.5 gra

16 The Ferm Surfae Iegrad /E p ploed over he frs Brllou zoe for UVδ δ p µ p

17 The Superoduor Phase Dagram The superoduor groud sae phase dagram he spae of elero des ad δ 3. Ise: Dfferee of groud sae eerges W p -W d vs W [ ] η E V η η ε η µ 4Λ V U 4

18 The Jump of he Spef Hea a Cral emperaure vs for a ssem wh arbrar U Vδ δ The se show DOS vs E for 0.09 gre le ad 0.6 bla le. b The ump for d squares ad p rles smmeres as a fuo of.

19 Elero Spef Hea vs T I low emperaures he spef hea s ver sesve o he odal les d squares ad p rles smmeres

20 The Cral Temperaure for he Asorop Superoduv 0 - * s-wave B T / If U grows s* drop d-wave L.A. Pérez P e al. Phsa B p-wave Vδ δ ad U8 0.

21 The Elero Spef Hea C α /C T α T/ α T/T α s 0.5 α p α d J.S. Mlla e al. Proeedgs of AIP T/T Vδ δ p ad.4 d. The s smmer wh U

22 Comparso of Theor ad Experme.0 C p /TC T J.S. Mlla e al. Proeedgs of AIP S.Nshza Nshza e al. J. Phs. So. Jp T/T Adus for p smmer wh Vδ δ ad wh he expermeal daa sold ragles of Sr RuO 4.

23 Comparso of Theor ad Experme T CeT/TCeT x0.4 x0.0 x eV eV La -x Sr x CuO 4 T. Masuza N. Momoo M. Oda ad M. Ido J. Phs. So. Jp dw-wavewave for UVδ δ T

24 The Sgle Parle Exao Eerg Gap / x The double of he mmal eerge erg order o brea a Cooper par r 0 as a fuo o of polar agle θa - / x for dw-wave wave wh UV Vδ δ ad he orrespodg Ferm Surfae.

25 CONCLUSIONS. The researh of sgle ad rple superoduv suggess he posbl of a ufed heor abou he Asorop Superoduv for p ad d smmeres a square lae.. The p- ad d-wave superoduv are respevel ehae he low ad hgh elero des regme. 3. We a fd approprae se of Hamloa parameers order o fd he elero spef hea ha mahes ver well wh expermeal daa. 4. The we have ow he possbl o assoae heor ad expermes of some maerals.

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state)

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state) Pro. O. B. Wrgh, Auum Quaum Mechacs II Lecure Tme-depede perurbao heory Tme-depede perurbao heory (degeerae or o-degeerae sarg sae) Cosder a sgle parcle whch, s uperurbed codo wh Hamloa H, ca exs a superposo

More information

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3.

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3. C. Trael me cures for mulple reflecors The ray pahs ad rael mes for mulple layers ca be compued usg ray-racg, as demosraed Lab. MATLAB scrp reflec_layers_.m performs smple ray racg. (m) ref(ms) ref(ms)

More information

Chebyshev Polynomials for Solving a Class of Singular Integral Equations

Chebyshev Polynomials for Solving a Class of Singular Integral Equations Appled Mahemas, 4, 5, 75-764 Publshed Ole Marh 4 SRes. hp://www.srp.org/joural/am hp://d.do.org/.46/am.4.547 Chebyshev Polyomals for Solvg a Class of Sgular Iegral Equaos Samah M. Dardery, Mohamed M. Alla

More information

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits. ose ad Varably Homewor # (8), aswers Q: Power spera of some smple oses A Posso ose A Posso ose () s a sequee of dela-fuo pulses, eah ourrg depedely, a some rae r (More formally, s a sum of pulses of wdh

More information

(1) Cov(, ) E[( E( ))( E( ))]

(1) Cov(, ) E[( E( ))( E( ))] Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )

More information

On Metric Dimension of Two Constructed Families from Antiprism Graph

On Metric Dimension of Two Constructed Families from Antiprism Graph Mah S Le 2, No, -7 203) Mahemaal Sees Leers A Ieraoal Joural @ 203 NSP Naural Sees Publhg Cor O Mer Dmeso of Two Cosrued Famles from Aprm Graph M Al,2, G Al,2 ad M T Rahm 2 Cere for Mahemaal Imagg Tehques

More information

Partial Molar Properties of solutions

Partial Molar Properties of solutions Paral Molar Properes of soluos A soluo s a homogeeous mxure; ha s, a soluo s a oephase sysem wh more ha oe compoe. A homogeeous mxures of wo or more compoes he gas, lqud or sold phase The properes of a

More information

Learning of Graphical Models Parameter Estimation and Structure Learning

Learning of Graphical Models Parameter Estimation and Structure Learning Learg of Grahal Models Parameer Esmao ad Sruure Learg e Fukumzu he Isue of Sasal Mahemas Comuaoal Mehodology Sasal Iferee II Work wh Grahal Models Deermg sruure Sruure gve by modelg d e.g. Mxure model

More information

Circular birefringence in crystal optics

Circular birefringence in crystal optics Crular brefrgee rsal ops R J Poo a) Joule Phss Laboraor Shool of Compug See ad geerg Maerals ad Phss Researh Cere Uvers of Salford Greaer Maheser M5 4WT UK. Absra I rsal ops he speal saus of he res frame

More information

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad

More information

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall 8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model

More information

Efficient Estimators for Population Variance using Auxiliary Information

Efficient Estimators for Population Variance using Auxiliary Information Global Joural of Mahemacal cece: Theor ad Praccal. IN 97-3 Volume 3, Number (), pp. 39-37 Ieraoal Reearch Publcao Houe hp://www.rphoue.com Effce Emaor for Populao Varace ug Aular Iformao ubhah Kumar Yadav

More information

Nonsmooth Optimization Algorithms in Some Problems of Fracture Dynamics

Nonsmooth Optimization Algorithms in Some Problems of Fracture Dynamics Iellge Iformao Maageme, 2, 2, 637-646 do:.4236/m.2.273 Publshed Ole November 2 (hp://www.srp.org/joural/m) Nosmooh Opmzao Algorhms Some Problems of Fraure Dyams Absra V. V. Zozulya Cero de Ivesgao Cefa

More information

Probability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract

Probability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract Probably Bracke Noao ad Probably Modelg Xg M. Wag Sherma Vsual Lab, Suyvale, CA 94087, USA Absrac Ispred by he Drac oao, a ew se of symbols, he Probably Bracke Noao (PBN) s proposed for probably modelg.

More information

c-field descriptions of nonequilibrium polariton fluids

c-field descriptions of nonequilibrium polariton fluids c-feld descrpos of oequlbrum polaro fluds Mchel Wouers Iacopo Carusoo, Vcezo Savoa polaro characerscs Dsperso D / 1D / 0D Ieracos g=0.01 1 ev 1 mm -1 1 mev homogeeous broadeg (bu log lfe me) 0.x mev Polaro

More information

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model . Projec Iroduco Fudameals of Speech Recogo Suggesed Projec The Hdde Markov Model For hs projec, s proposed ha you desg ad mpleme a hdde Markov model (HMM) ha opmally maches he behavor of a se of rag sequeces

More information

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS THE PREICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS INTROUCTION The wo dmensonal paral dfferenal equaons of second order can be used for he smulaon of compeve envronmen n busness The arcle presens he

More information

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions: Paramerc coug process models Cosder coug processes: N,,..., ha cou he occurreces of a eve of eres for dvduals Iesy processes: Lelhood λ ( ;,,..., N { } λ < Log-lelhood: l( log L( Score fucos: U ( l( log

More information

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo

More information

High Tc superconductivity in cuprates: Determination of pairing interaction. Han-Yong Choi / SKKU SNU Colloquium May 30, 2018

High Tc superconductivity in cuprates: Determination of pairing interaction. Han-Yong Choi / SKKU SNU Colloquium May 30, 2018 High Tc superconductivity in cuprates: Determination of pairing interaction Han-Yong Choi / SKKU SNU Colloquium May 30 018 It all began with Discovered in 1911 by K Onnes. Liquid He in 1908. Nobel prize

More information

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles Ope Joural of Dsree Mahemas 2017 7 200-217 hp://wwwsrporg/joural/ojdm ISSN Ole: 2161-7643 ISSN Pr: 2161-7635 Cylally Ierval Toal Colorgs of Cyles Mddle Graphs of Cyles Yogqag Zhao 1 Shju Su 2 1 Shool of

More information

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes. umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal

More information

Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models

Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models Ieraoal Bomerc Coferece 22/8/3, Kobe JAPAN Survval Predco Based o Compoud Covarae uder Co Proporoal Hazard Models PLoS ONE 7. do:.37/oural.poe.47627. hp://d.plos.org/.37/oural.poe.47627 Takesh Emura Graduae

More information

d x 2 ±y 2 pairing in the generalized Hubbard square-lattice model

d x 2 ±y 2 pairing in the generalized Hubbard square-lattice model PERGAMON Solid State Communications 118 (2001) 589±593 www.elsevier.com/locate/ssc d x 2 ±y 2 pairing in the generalized Hubbard square-lattice model Luis A. PeÂrez, Chumin Wang* Instituto de Investigaciones

More information

Quantum many-body systems and tensor networks: simulation methods and applications

Quantum many-body systems and tensor networks: simulation methods and applications Quantum many-body systems and tensor networks: simulation methods and applications Román Orús School of Physical Sciences, University of Queensland, Brisbane (Australia) Department of Physics and Astronomy,

More information

Spin-Charge Separation in 1-D. Spin-Charge Separation in 1-D. Spin-Charge Separation - Experiment. Spin-Charge Separation - Experiment

Spin-Charge Separation in 1-D. Spin-Charge Separation in 1-D. Spin-Charge Separation - Experiment. Spin-Charge Separation - Experiment Spin-Charge Separation in 1-D Lecture: Solvable 1D electron systems, Mott insulator and correlated electron systems in 2D Solid State Spectroscopy Course 25/2/2013 Spin : J Charge : t Decay of a photohole

More information

Chapter 3: Vectors and Two-Dimensional Motion

Chapter 3: Vectors and Two-Dimensional Motion Chape 3: Vecos and Two-Dmensonal Moon Vecos: magnude and decon Negae o a eco: eese s decon Mulplng o ddng a eco b a scala Vecos n he same decon (eaed lke numbes) Geneal Veco Addon: Tangle mehod o addon

More information

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS Vol.7 No.4 (200) p73-78 Joural of Maageme Scece & Sascal Decso IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS TIANXIANG YAO AND ZAIWU GONG College of Ecoomcs &

More information

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA QR facorzao Ay x real marx ca be wre as AQR, where Q s orhogoal ad R s upper ragular. To oba Q ad R, we use he Householder rasformao as follows: Le P, P, P -, be marces such ha P P... PPA ( R s upper ragular.

More information

Financial Econometrics Jeffrey R. Russell Midterm Winter 2009 SOLUTIONS

Financial Econometrics Jeffrey R. Russell Midterm Winter 2009 SOLUTIONS Name SOLUTIONS Financial Economerics Jeffrey R. Russell Miderm Winer 009 SOLUTIONS You have 80 minues o complee he exam. Use can use a calculaor and noes. Try o fi all your work in he space provided. If

More information

Analytical modelling of extruded plates

Analytical modelling of extruded plates paper ID: 56 /p. Aalcal modellg of erded plaes C. Pézera, J.-L. Gader Laboraore Vbraos Acosqe, INSA de Lo,5 bs a.j. Cappelle 696 VILLEURBANNE Cede Erded plaes are ofe sed o bld lgh srcres h hgh sffess.

More information

ANALYSIS OF FLUID-SATURATED POROUS MEDIA IN TWO DIMENSIONS UNDER EARTHQUAKE LOAD

ANALYSIS OF FLUID-SATURATED POROUS MEDIA IN TWO DIMENSIONS UNDER EARTHQUAKE LOAD ANALYI O LI-ATATE POO MEIA IN TWO IMENION NE EATHQAKE LOA Xoj QIN hol CHEN Ad Xh ZEN 3 MMAY The lss of d rse pheoe fld-sred poros ed s of gre eres geoehl egeerg d egeerg sesolog. I he prese pper he respose

More information

Midterm Exam. Tuesday, September hour, 15 minutes

Midterm Exam. Tuesday, September hour, 15 minutes Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.

More information

Cyclone. Anti-cyclone

Cyclone. Anti-cyclone Adveco Cycloe A-cycloe Lorez (963) Low dmesoal aracors. Uclear f hey are a good aalogy o he rue clmae sysem, bu hey have some appealg characerscs. Dscusso Is he al codo balaced? Is here a al adjusme

More information

Superconductivity Induced Transparency

Superconductivity Induced Transparency Superconductivity Induced Transparency Coskun Kocabas In this paper I will discuss the effect of the superconducting phase transition on the optical properties of the superconductors. Firstly I will give

More information

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations Joural of aheacs ad copuer Scece (4 39-38 Soluo of Ipulsve Dffereal Equaos wh Boudary Codos Ters of Iegral Equaos Arcle hsory: Receved Ocober 3 Acceped February 4 Avalable ole July 4 ohse Rabba Depare

More information

EE 6885 Statistical Pattern Recognition

EE 6885 Statistical Pattern Recognition EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://www.ee.columba.edu/~sfchag Lecure 5 (9//05 4- Readg Model Parameer Esmao ML Esmao, Chap. 3. Mure of Gaussa ad EM Referece Boo, HTF Chap. 8.5 Teboo,

More information

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,

More information

Solid State Device Fundamentals

Solid State Device Fundamentals Sold State Devce Fudametals 9 polar jucto trasstor Sold State Devce Fudametals 9. polar Jucto Trasstor NS 345 Lecture ourse by Alexader M. Zatsev alexader.zatsev@cs.cuy.edu Tel: 718 98 81 4N101b Departmet

More information

Microwave-induced Thermoacoustic Imaging for Radio-frequency Tumor Ablation: a Hybrid FDTD Modeling and Experimental study

Microwave-induced Thermoacoustic Imaging for Radio-frequency Tumor Ablation: a Hybrid FDTD Modeling and Experimental study Mcrowave-duced Theroacousc Iagg for Rado-frequec Tuor Ablao: a Hbrd FDTD Modelg ad Epereal sud Yg Deg ad Mark Golkowsk Depare of Elecrcal Egeerg Depare of Boegeerg Uvers of Colorado Dever Dowow EE Naoal

More information

Probing the Electronic Structure of Complex Systems by State-of-the-Art ARPES Andrea Damascelli

Probing the Electronic Structure of Complex Systems by State-of-the-Art ARPES Andrea Damascelli Probing the Electronic Structure of Complex Systems by State-of-the-Art ARPES Andrea Damascelli Department of Physics & Astronomy University of British Columbia Vancouver, B.C. Outline: Part I State-of-the-Art

More information

1 Widrow-Hoff Algorithm

1 Widrow-Hoff Algorithm COS 511: heoreical Machine Learning Lecurer: Rob Schapire Lecure # 18 Scribe: Shaoqing Yang April 10, 014 1 Widrow-Hoff Algorih Firs le s review he Widrow-Hoff algorih ha was covered fro las lecure: Algorih

More information

Final Exam Applied Econometrics

Final Exam Applied Econometrics Fal Eam Appled Ecoomercs. 0 Sppose we have he followg regresso resl: Depede Varable: SAT Sample: 437 Iclded observaos: 437 Whe heeroskedasc-cosse sadard errors & covarace Varable Coeffce Sd. Error -Sasc

More information

Solution set Stat 471/Spring 06. Homework 2

Solution set Stat 471/Spring 06. Homework 2 oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o

More information

RATIO ESTIMATORS USING CHARACTERISTICS OF POISSON DISTRIBUTION WITH APPLICATION TO EARTHQUAKE DATA

RATIO ESTIMATORS USING CHARACTERISTICS OF POISSON DISTRIBUTION WITH APPLICATION TO EARTHQUAKE DATA The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 Absrac RATIO ESTIMATORS USING HARATERISTIS OF POISSON ISTRIBUTION WITH APPLIATION TO EARTHQUAKE ATA Gamze Özel Naural pulaos bolog geecs educao

More information

Comprehensive Integrated Simulation and Optimization of LPP for EUV Lithography Devices

Comprehensive Integrated Simulation and Optimization of LPP for EUV Lithography Devices Comprehense Inegraed Smulaon and Opmaon of LPP for EUV Lhograph Deces A. Hassanen V. Su V. Moroo T. Su B. Rce (Inel) Fourh Inernaonal EUVL Smposum San Dego CA Noember 7-9 2005 Argonne Naonal Laboraor Offce

More information

The Poisson Process Properties of the Poisson Process

The Poisson Process Properties of the Poisson Process Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad

More information

ME 160 Introduction to Finite Element Method. Chapter 5 Finite Element Analysis in Heat Conduction Analysis of Solid Structures

ME 160 Introduction to Finite Element Method. Chapter 5 Finite Element Analysis in Heat Conduction Analysis of Solid Structures San Jose Sae Unvers Deparmen o Mehanal Engneerng ME 6 Inroduon o Fne Elemen Mehod Chaper 5 Fne Elemen Analss n Hea Conduon Analss o Sold Sruures Insruor a-ran Hsu Proessor Prnpal reerenes: ) he Fne Elemen

More information

Lagrangian & Hamiltonian Mechanics:

Lagrangian & Hamiltonian Mechanics: XII AGRANGIAN & HAMITONIAN DYNAMICS Iouco Hamlo aaoal Pcple Geealze Cooaes Geealze Foces agaga s Euao Geealze Momea Foces of Cosa, agage Mulples Hamloa Fucos, Cosevao aws Hamloa Dyamcs: Hamlo s Euaos agaga

More information

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination Lecure 3 Topc : Drbuo, hypohe eg, ad ample ze deermao The Sude - drbuo Coder a repeaed drawg of ample of ze from a ormal drbuo of mea. For each ample, compue,,, ad aoher ac,, where: The ac he devao of

More information

Quantum Processes in Josephson Junctions & Weak Links. J. A. Sauls

Quantum Processes in Josephson Junctions & Weak Links. J. A. Sauls CMS Colloquium, Los Alamos National Laboratory, December 9, 2015 Quantum Processes in Josephson Junctions & Weak Links J. A. Sauls Northwestern University e +iφ 2 e +iφ 1 111000 00000000 111111110000000

More information

The far field calculation: Approximate and exact solutions. Persa Kyritsi November 10th, 2005 B2-109

The far field calculation: Approximate and exact solutions. Persa Kyritsi November 10th, 2005 B2-109 Th fa fl calculao: Appoa a ac oluo Pa K Novb 0h 005 B-09 Oul Novb 0h 005 Pa K Iouco Appoa oluo flco fo h gou ac oluo Cocluo Pla wav fo Ic fl: pla wav k ( ) jk H ( ) λ λ ( ) Polaao fo η 0 0 Hooal polaao

More information

An Exact Solution for the Differential Equation. Governing the Lateral Motion of Thin Plates. Subjected to Lateral and In-Plane Loadings

An Exact Solution for the Differential Equation. Governing the Lateral Motion of Thin Plates. Subjected to Lateral and In-Plane Loadings Appled Mahemacal Sceces, Vol., 8, o. 34, 665-678 A Eac Soluo for he Dffereal Equao Goverg he Laeral Moo of Th Plaes Subjeced o Laeral ad I-Plae Loadgs A. Karmpour ad D.D. Gaj Mazadara Uvers Deparme of

More information

APPLICATION OF A Z-TRANSFORMS METHOD FOR INVESTIGATION OF MARKOV G-NETWORKS

APPLICATION OF A Z-TRANSFORMS METHOD FOR INVESTIGATION OF MARKOV G-NETWORKS Joa of Aed Mahema ad Comaoa Meha 4 3( 6-73 APPLCATON OF A Z-TRANSFORMS METHOD FOR NVESTGATON OF MARKOV G-NETWORKS Mha Maay Vo Nameo e of Mahema Ceohowa Uey of Tehoogy Cęohowa Poad Fay of Mahema ad Come

More information

The critical temperature of superconductor and its electronic specific heat

The critical temperature of superconductor and its electronic specific heat arxiv:1008.1446v1 [physics.gen-ph] 9 Aug 2010 The critical temperature of superconductor and its electronic specific heat 1 Introduction B.V.Vasiliev The task of the critical parameters of a superconductor

More information

SHANGHAI JIAO TONG UNIVERSITY LECTURE

SHANGHAI JIAO TONG UNIVERSITY LECTURE Lecture 7 SHANGHAI JIAO TONG UNIVERSITY LECTURE 7 017 Anthony J. Leggett Department of Physics University of Illinois at Urbana-Champaign, USA and Director, Center for Complex Physics Shanghai Jiao Tong

More information

[ns] ATLAS Preliminary. [GeV] Observed 95% CL limit (±1 theory. ) Expected 95% CL limit (±1 exp ) ATLAS, Eur.Phys.J.C

[ns] ATLAS Preliminary. [GeV] Observed 95% CL limit (±1 theory. ) Expected 95% CL limit (±1 exp ) ATLAS, Eur.Phys.J.C A A" A" τ χ±!τ χ± - L dt = 4.7 fb, s = 7 ev SUSY Observed 95% CL limit (± theory ) Expected 95% CL limit (± exp ) ALAS, Eur.Phys.J.C72 993 - (m < 32 ev, m

More information

Scattering at an Interface: Oblique Incidence

Scattering at an Interface: Oblique Incidence Course Insrucor Dr. Raymond C. Rumpf Offce: A 337 Phone: (915) 747 6958 E Mal: rcrumpf@uep.edu EE 4347 Appled Elecromagnecs Topc 3g Scaerng a an Inerface: Oblque Incdence Scaerng These Oblque noes may

More information

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s A g la di ou s F. L. 462 E l ec tr on ic D ev el op me nt A i ng er A.W.S. 371 C. A. M. A l ex an de r 236 A d mi ni st ra ti on R. H. (M rs ) A n dr ew s P. V. 326 O p ti ca l Tr an sm is si on A p ps

More information

EMA5001 Lecture 3 Steady State & Nonsteady State Diffusion - Fick s 2 nd Law & Solutions

EMA5001 Lecture 3 Steady State & Nonsteady State Diffusion - Fick s 2 nd Law & Solutions EMA5 Lecue 3 Seady Sae & Noseady Sae ffuso - Fck s d Law & Soluos EMA 5 Physcal Popees of Maeals Zhe heg (6) 3 Noseady Sae ff Fck s d Law Seady-Sae ffuso Seady Sae Seady Sae = Equlbum? No! Smlay: Sae fuco

More information

Moments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables

Moments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables Joural of Mahemacs ad Sascs 6 (4): 442-448, 200 SSN 549-3644 200 Scece Publcaos Momes of Order Sascs from Nodecally Dsrbued Three Parameers Bea ype ad Erlag Trucaed Expoeal Varables A.A. Jamoom ad Z.A.

More information

COMPUTER SCIENCE 349A SAMPLE EXAM QUESTIONS WITH SOLUTIONS PARTS 1, 2

COMPUTER SCIENCE 349A SAMPLE EXAM QUESTIONS WITH SOLUTIONS PARTS 1, 2 COMPUTE SCIENCE 49A SAMPLE EXAM QUESTIONS WITH SOLUTIONS PATS, PAT.. a Dene he erm ll-ondoned problem. b Gve an eample o a polynomal ha has ll-ondoned zeros.. Consder evaluaon o anh, where e e anh. e e

More information

Lecture 10 - Model Identification

Lecture 10 - Model Identification Lecure - odel Idenificaion Wha is ssem idenificaion? Direc impulse response idenificaion Linear regression Regularizaion Parameric model ID nonlinear LS Conrol Engineering - Wha is Ssem Idenificaion? Experimen

More information

ARWtr 2004 Modern Transformers October. Vigo Spain Transformers

ARWtr 2004 Modern Transformers October. Vigo Spain   Transformers The procedure s bes explaned by Fg. 1a + 1b 1U 1V 1W 1N HV S U 0 LV 2V 2U -nalyser-3205 2W Fg. 1a. Measuremen of he relaxaon currens usng he Semens measurng sysem -nalyser-3205 [14, 20] U 0 u T P T D POL

More information

A Mean- maximum Deviation Portfolio Optimization Model

A Mean- maximum Deviation Portfolio Optimization Model A Mea- mamum Devato Portfolo Optmzato Model Wu Jwe Shool of Eoom ad Maagemet, South Cha Normal Uversty Guagzhou 56, Cha Tel: 86-8-99-6 E-mal: wujwe@9om Abstrat The essay maes a thorough ad systemat study

More information

Strauss PDEs 2e: Section Exercise 1 Page 1 of 6

Strauss PDEs 2e: Section Exercise 1 Page 1 of 6 Strauss PDEs e: Setion.1 - Exerise 1 Page 1 of 6 Exerise 1 Solve u tt = u xx, u(x, 0) = e x, u t (x, 0) = sin x. Solution Solution by Operator Fatorization By fatoring the wave equation and making a substitution,

More information

Coherent Potential Approximation

Coherent Potential Approximation Coheret Potetal Approxato Noveber 29, 2009 Gree-fucto atrces the TB forals I the tght bdg TB pcture the atrx of a Haltoa H s the for H = { H j}, where H j = δ j ε + γ j. 2 Sgle ad double uderles deote

More information

Electromagnetic waves in vacuum.

Electromagnetic waves in vacuum. leromagne waves n vauum. The dsovery of dsplaemen urrens enals a peular lass of soluons of Maxwell equaons: ravellng waves of eler and magne felds n vauum. In he absene of urrens and harges, he equaons

More information

MATH 220: Problem Set 3 Solutions

MATH 220: Problem Set 3 Solutions MATH 220: Problem Set 3 Solutions Problem 1. Let ψ C() be given by: 0, x < 1, 1 + x, 1 < x < 0, ψ(x) = 1 x, 0 < x < 1, 0, x > 1, so that it verifies ψ 0, ψ(x) = 0 if x 1 and ψ(x)dx = 1. Consider (ψ j )

More information

High-Temperature Superconductors: Playgrounds for Broken Symmetries

High-Temperature Superconductors: Playgrounds for Broken Symmetries High-Temperature Superconductors: Playgrounds for Broken Symmetries Gauge / Phase Reflection Time Laura H. Greene Department of Physics Frederick Seitz Materials Research Laboratory Center for Nanoscale

More information

P a g e 3 6 of R e p o r t P B 4 / 0 9

P a g e 3 6 of R e p o r t P B 4 / 0 9 P a g e 3 6 of R e p o r t P B 4 / 0 9 p r o t e c t h um a n h e a l t h a n d p r o p e r t y fr om t h e d a n g e rs i n h e r e n t i n m i n i n g o p e r a t i o n s s u c h a s a q u a r r y. J

More information

Analysis Of Clustering Algorithms for MR Image Segmentation Using IQI

Analysis Of Clustering Algorithms for MR Image Segmentation Using IQI Avalable ole a www.seedre.o Proeda Teholog 6 (0 ) 387 396 d Ieraoal Coferee o Couao, Copug & Seur [ICCCS-0] Aalss Of Cluserg Algorhs for MR Iage Segeao Usg IQI S. Pael, K.S.Paa Depare of Copuer See ad

More information

Extreme scale simulations of high-temperature superconductivity. Thomas C. Schulthess

Extreme scale simulations of high-temperature superconductivity. Thomas C. Schulthess Extreme scale simulations of high-temperature superconductivity Thomas C. Schulthess T [K] Superconductivity: a state of matter with zero electrical resistivity Heike Kamerlingh Onnes (1853-1926) Discovery

More information

lectures accompanying the book: Solid State Physics: An Introduction, by Philip ofmann (2nd edition 2015, ISBN-10: 3527412824, ISBN-13: 978-3527412822, Wiley-VC Berlin. www.philiphofmann.net 1 Bonds between

More information

κt π = (5) T surrface k BASELINE CASE

κt π = (5) T surrface k BASELINE CASE II. BASELINE CASE PRACICAL CONSIDERAIONS FOR HERMAL SRESSES INDUCED BY SURFACE HEAING James P. Blanhard Universi of Wisonsin Madison 15 Engineering Dr. Madison, WI 5376-169 68-63-391 blanhard@engr.is.edu

More information

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent oucms Couga Gas Mchal Kazha (6.657) Ifomao abou h Sma (6.757) hav b pos ol: hp://www.cs.hu.u/~msha Tch Spcs: o M o Tusay afoo. o Two paps scuss ach w. o Vos fo w s caa paps u by Thusay vg. Oul Rvw of Sps

More information

Dynamical properties of strongly correlated electron systems studied by the density-matrix renormalization group (DMRG) Takami Tohyama

Dynamical properties of strongly correlated electron systems studied by the density-matrix renormalization group (DMRG) Takami Tohyama Dynamical properties of strongly correlated electron systems studied by the density-matrix renormalization group (DMRG) Takami Tohyama Tokyo University of Science Shigetoshi Sota AICS, RIKEN Outline Density-matrix

More information

COMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION

COMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION COMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION Eldesoky E. Affy. Faculy of Eg. Shbee El kom Meoufa Uv. Key word : Raylegh dsrbuo, leas squares mehod, relave leas squares, leas absolue

More information

Photoemission Studies of Strongly Correlated Systems

Photoemission Studies of Strongly Correlated Systems Photoemission Studies of Strongly Correlated Systems Peter D. Johnson Physics Dept., Brookhaven National Laboratory JLab March 2005 MgB2 High T c Superconductor - Phase Diagram Fermi Liquid:-Excitations

More information

Chapter 8. Simple Linear Regression

Chapter 8. Simple Linear Regression Chaper 8. Smple Lear Regresso Regresso aalyss: regresso aalyss s a sascal mehodology o esmae he relaoshp of a respose varable o a se of predcor varable. whe here s jus oe predcor varable, we wll use smple

More information

Exotic Properties of Superconductor- Ferromagnet Structures.

Exotic Properties of Superconductor- Ferromagnet Structures. SMR.1664-16 Conference on Single Molecule Magnets and Hybrid Magnetic Nanostructures 27 June - 1 July 2005 ------------------------------------------------------------------------------------------------------------------------

More information

Application of GA Based Fuzzy Neural Networks for Measuring Fouling in Condenser

Application of GA Based Fuzzy Neural Networks for Measuring Fouling in Condenser pplao of G Based Fuzzy eural eors for Measur Foul Codeser Fa Shao-she Chasha Uversy of See ad Teholoy Chasha 40077 Cha fss508@63.om Wa Yao-a Hua Uversy Chasha 4008 Cha yaoa@63.om bsra - ovel approah for

More information

ANTIFERROMAGNETIC EXCHANGE AND SPIN-FLUCTUATION PAIRING IN CUPRATES

ANTIFERROMAGNETIC EXCHANGE AND SPIN-FLUCTUATION PAIRING IN CUPRATES ANTIFERROMAGNETIC EXCHANGE AND SPIN-FLUCTUATION PAIRING IN CUPRATES N.M.Plakida Joint Institute for Nuclear Research, Dubna, Russia CORPES, Dresden, 26.05.2005 Publications and collaborators: N.M. Plakida,

More information

P a g e 5 1 of R e p o r t P B 4 / 0 9

P a g e 5 1 of R e p o r t P B 4 / 0 9 P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e

More information

Spettroscopia risonante di stati elettronici: un approccio impossibile senza i sincrotroni

Spettroscopia risonante di stati elettronici: un approccio impossibile senza i sincrotroni Spettroscopia risonante di stati elettronici: un approccio impossibile senza i sincrotroni XAS, XMCD, XES, RIXS, ResXPS: introduzione alle spettroscopie risonanti * Dipartimento di Fisica - Politecnico

More information

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Reliability Analysis

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Reliability Analysis Probably /4/6 CS 5 elably Aaly Yahwa K. Malaya Colorado Sae very Ocober 4, 6 elably Aaly: Oule elably eaure: elably, avalably, Tra. elably, T M MTTF ad (, MTBF Bac Cae Sgle u wh perae falure, falure rae

More information

Collocation Method for Nonlinear Volterra-Fredholm Integral Equations

Collocation Method for Nonlinear Volterra-Fredholm Integral Equations Ope Joural of Appled Sees 5- do:436/oapps6 Publshed Ole Jue (hp://wwwsrporg/oural/oapps) Colloao Mehod for olear Volerra-Fredhol Iegral Equaos Jafar Ahad Shal Parvz Daraa Al Asgar Jodayree Akbarfa Depare

More information

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!) i+1,q - [(! ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal

More information

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N

More information

T h e C S E T I P r o j e c t

T h e C S E T I P r o j e c t T h e P r o j e c t T H E P R O J E C T T A B L E O F C O N T E N T S A r t i c l e P a g e C o m p r e h e n s i v e A s s es s m e n t o f t h e U F O / E T I P h e n o m e n o n M a y 1 9 9 1 1 E T

More information

Discrete Markov Process. Introduction. Example: Balls and Urns. Stochastic Automaton. INTRODUCTION TO Machine Learning 3rd Edition

Discrete Markov Process. Introduction. Example: Balls and Urns. Stochastic Automaton. INTRODUCTION TO Machine Learning 3rd Edition EHEM ALPAYDI he MI Press, 04 Lecure Sldes for IRODUCIO O Machne Learnng 3rd Edon alpaydn@boun.edu.r hp://www.cmpe.boun.edu.r/~ehem/ml3e Sldes from exboo resource page. Slghly eded and wh addonal examples

More information

CLASSIFICATION AND PRINCIPLE OF SUPERPOSITION FOR SECOND ORDER LINEAR PDE

CLASSIFICATION AND PRINCIPLE OF SUPERPOSITION FOR SECOND ORDER LINEAR PDE CLASSIFICATION AND PRINCIPLE OF SUPERPOSITION FOR SECOND ORDER LINEAR PDE 1. Linear Partial Differential Equations A partial differential equation (PDE) is an equation, for an unknown function u, that

More information

Marks for each question are as indicated in [] brackets.

Marks for each question are as indicated in [] brackets. Name Student Number CHEMISTRY 140 FINAL EXAM December 10, 2002 Numerical answers must be given with appropriate units and significant figures. Please place all answers in the space provided for the question.

More information

Effect of Electric Field on Ferroelectric and Dielectric Properties in Rochelle Salt Crystal

Effect of Electric Field on Ferroelectric and Dielectric Properties in Rochelle Salt Crystal DOI:0.7598/cs07.65 Chemcal cece Trasacos IN:78-58 07 6 66-7 REERCH RTICLE Effec of Elecrc Feld o Ferroelecrc ad Delecrc Properes Rochelle al Crysal KLIK PRD EMWL ad TRILOK CHNDR UPDHYY Physcs Deparme H.N.B.

More information

NDP4050L / NDB4050L N-Channel Logic Level Enhancement Mode Field Effect Transistor

NDP4050L / NDB4050L N-Channel Logic Level Enhancement Mode Field Effect Transistor April 996 NP45L / NB45L N-Channel Logic Level Enhancemen Mode Field Effec Transisor General escripion Feaures These logic level N-Channel enhancemen mode power field effec ransisors are produced using

More information

Continuous Time Markov Chains

Continuous Time Markov Chains Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,

More information

CHEM 172 EXAMINATION 1. January 15, 2009

CHEM 172 EXAMINATION 1. January 15, 2009 CHEM 17 EXAMINATION 1 January 15, 009 Dr. Kimberly M. Broekemeier NAME: Circle lecture time: 9:00 11:00 Constants: c = 3.00 X 10 8 m/s h = 6.63 X 10-34 J x s J = kg x m /s Rydberg Constant = 1.096776 x

More information

4. THE DENSITY MATRIX

4. THE DENSITY MATRIX 4. THE DENSTY MATRX The desy marx or desy operaor s a alerae represeao of he sae of a quaum sysem for whch we have prevously used he wavefuco. Alhough descrbg a quaum sysem wh he desy marx s equvale o

More information

Density estimation III. Linear regression.

Density estimation III. Linear regression. Lecure 6 Mlos Hauskrec mlos@cs.p.eu 539 Seo Square Des esmao III. Lear regresso. Daa: Des esmao D { D D.. D} D a vecor of arbue values Obecve: r o esmae e uerlg rue probabl srbuo over varables X px usg

More information

Mathematical Formulation

Mathematical Formulation Mahemacal Formulao The purpose of a fe fferece equao s o appromae he paral ffereal equao (PE) whle maag he physcal meag. Eample PE: p c k FEs are usually formulae by Taylor Seres Epaso abou a po a eglecg

More information