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making-sense

Course: MATH 2112, Fall 2009
School: UCF
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Sense Tom Making Carter http://astarte.csustan.edu/~ tom/SFI-CSSS April 2, 2009 1 Making Sense Introduction / theme / structure 3 Language and meaning Language and meaning (ex) . . . . . . . . . . . . . . . 6 7 Theories, models and simulation Theories, models and simulation (ex) . . . . . . . . . . 8 28 References 29 2 Introduction / theme / structure This is a brief introduction to some thoughts...

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Sense Tom Making Carter http://astarte.csustan.edu/~ tom/SFI-CSSS April 2, 2009 1 Making Sense Introduction / theme / structure 3 Language and meaning Language and meaning (ex) . . . . . . . . . . . . . . . 6 7 Theories, models and simulation Theories, models and simulation (ex) . . . . . . . . . . 8 28 References 29 2 Introduction / theme / structure This is a brief introduction to some thoughts about making sense of the world. We all make sense of the world in many ways all the time, but we don't always do it consciously, nor do we necessarily have rigorous / structured approaches to the project . . . My goal here is to talk some about how I go about building theories, models and simulations that I can use to make the world make more sense to me and also that I can use as parts of explanations to help others see the world in potentially more useful ways. As a teacher/researcher, I'm always looking for more illumination, and better ways to reveal that illumination. 3 One important point for me is that in general in these sorts of projects, I am much more interested in epistemology than in ontology. Just briefly, what are ontology and epistemology? I tend to think about them this way: Ontology: "What is there?" (i.e., questions of "being") Epistemology: "How do we know?" (i.e., questions of "knowledge") In many respects, I see these as two of the main (or the main two) branches of philosophy, and especially of philosophy of science. They tend to have convergences and divergences, but they drive much philosphizing (and much argumentation over angels and heads of pins . . . :-) 4 Ontological questions tend to litter the fields of science: What are the fundamental elements? Are there just four of them? What is the Fifth Element? Is it the quintessence, or just a so-so sci-fi movie? Does caloric exist? Does phlogiston exist? Are there atoms, or not? Do electrons exist? Does the force of gravity exist? What is a gene? What is a species? What is life? Does Truth exist? For the most part, I think these questions are largely irrelevant. In many respects, I think they are very often the wrong kinds of questions for scientists to ask . . . Epistemological questions also abound: What can we know? How can we best go a trying to learn (gain knowledge) about the world? What role does evidence play in understanding systems? How meaningful is deduction within axiomatic contexts? 5 Language and meaning Hmmm . . . This is a placeholder for some things I want to write about, but haven't yet. Reference and Platonism Use and Wittgenstein What is meaning, and how does it happen? 6 Language and meaning - exercises 1. Explain why "meaning is use" is meaningful. 7 Theories, models and simulation What is a theory? This turns out (at least in a socio-cultural sense) to be a rather difficult question. One fairly traditional notion is to use ideas from mathematics. In this form, a theory is a collection of axioms, definitions, rules of inference, symbols, "objects," relations, etc. (e.g., the theory of Euclidean Geometry). One can (in theory :-) derive results within the theory, and engage in a general hypothetico-deductive cycle. One "makes a hypothesis" within the framework of the theory, and then checks to see if the hypothesis is derivable within the theory. A fundamental question one can ask at this stage is whether the theory is consistent i.e., whether the collection of axioms, etc., is logically consistent. 8 A straightforward example of this is the mathematical theory of Groups. A group is a quadruple (G, ,-1 , e), where G is a set (of group elements), : G G G is a binary operation, -1 : G G is a unary operation (the inverse in G), and e G is a distinguished element of G (the identity element) satisfying the axioms, for g, g1, g2, g3 G: 0. g1 g2 G and g -1 G (closure under and -1) 1. (g1 g2) g3 = g1 (g2 g3) (associativity) 2. e g = g e = g (identity element) 3. g g -1 = g -1 g = e (inverses) 9 (Note that we don't really need axiom 0, since we require that and -1 be a binary and unary operation (respectively) on G, but it is traditional to emphasize the closure property(s)). Given these axioms, we can prove various things. For example, the identity element e is unique: If a g = g for all g G, then a = a e = e (axiom 2, and then the property of a). Similarly, inverses are unique: if g a = a g = e, then a = a e = a (g g -1) = (a g) g -1 = e g -1 = g -1. The next step, then, would be to try to construct an interpretation of the theory (what might also be called a model). In general, this would be a mapping from "object" 10 and "relation" (etc.) symbols in the theory to specific "objects," "relations," etc., external to the theory. This process can sometimes be relatively straightforward, and other times remarkably problematic . . . For example, it is easy to check that the integers form a group under addition (i.e., the quadruple (Z, +, -, 0) satisfies the group axioms). We then have the nice property that any theorem we have proven using the group axioms is automatically satisfied by the integers. Similarly we can develop notions of symmetry groups and crystallographic groups, with a variety of interesting applications . . . 11 An observation we can make here is that the existence of a model of a given theory assures us that the theory is consistent. But a somewhat more subtle question is the Truth question. Does it make sense to ask whether Group Theory is True? I have to say, I can't really make sense of that question. It certainly is the case that Group Theory is consistent (it has models), and it is certainly very useful in many contexts, but True? I don't know . . . 12 Perhaps more interesting are attempts to construct interpretations to real-world objects and phenomena. Unfortunately, this can be quite subtle in particular, difficulty typically arises in confirming that the axioms actually hold in the interpretation. We can now enter into another level of hypothetico-deductive cycle. We make observations in the realm "external" to the "theory," then "turn the crank" to get "predictions," make more observations, and see if the results "match" (e.g., we "do experiments"). At this point, we are likely to have to make some sense of what we have seen, and decide what to do next. 13 Perhaps it would be worth going through a specific example, to see some of the issues. Imagine for a moment that it is around 1300 C.E., and you work for the Grand Vizier. He (and the King) believe that the planets affect one's life, and they demand that you "cast the King's horoscope." In other words, you are to describe (in some detail) what the sky would look at like a particular time and place (perhaps some 35 years prior). Your first "observation" is that most of the points of light (stars) in the night sky are "fixed." Of course, even this much requires a significant degree of abstraction if you hold your gaze fixed with respect to the ground you stand on, the stars will "move" they will "rotate" during the night. The stars are "fixed" with respect to each other. Notice that there is also a (covert) assumption that the 14 stars I see tonight are the same stars I saw last night. I can have my graduate students (apprentices :-) draw maps of the (relative) positions of the stars on successive nights, noting the strong similarities between the maps, I can then make the (simplifying) assumption that they are the same stars. Part of what I am pointing out here is that in building a "theory" there are innumerable background (often unspoken) assumptions necessarily underlying the "theory." It is probably worth noting that if the "theory" "doesn't work," it may be (is?) a nontrivial exercise to figure out which of the explicit and/or implicit assumptions might be changed to get the "theory" to "work" . . . We can now "observe" (with various caveats . . . ) that the planets "move" with respect to the fixed stars. Making various assumptions about regularity, continuity, and 15 simplicity in general, we want to build a "model" (an orrery?) of the (relative) motions of the planets. What are the constraints within which we will build our "model?" A first major constraint is that the planets, being celestial objects, will move in "perfect" ways, and since the circle is the most perfect of shapes, they will move along circular paths. Thus we start building our theory. Our theory: The fixed stars are on an encompassing immense sphere. The planets move on circular paths within the sphere. The planets move continuously, smoothly, and at a constant rate along their paths. Each (circular) path has a fixed center and a fixed radius. We now build a specific model. For each planet, we choose (determine) a specific center, radius, and rate of travel. Without much thought, the circles are all coplanar. 16 Here is a first picture: 17 Or, perhaps a better way to think about it: Here the observer (us) looks at the world through the theory/model (from within a perhaps unacknowledged paradigm). The theory/model becomes a lens through 18 which the world is viewed. This lens serves to select/emphasize certain aspects of the world. Notice that the "prediction" of the model does not exactly match the world . . . Now what do we do? Given that the "prediction" of our model does not exactly match the world, we have several choices. First, we could declare the match "good enough," take the money from the Grand Vizier, and go on our way. Second, we could modify our model, by changing various parameters (radii, centers, rate of motion, etc.). We could then check each of these revised models to see if one was "good enough." 19 We might notice that some particular aspects of the phenomena are not consistent with the model. For example, we might notice that the motion of Mars is sometimes retrograde: If none of the revised models was "good enough," we might replace our theory with a different one. In this case (remaining in the same "paradigm") we might "allow" our planets to move in "circles upon circles" (i.e., epicycles), so that sometimes the planet would move "backward." 20 Here's the next: We now have lots of parameters we can adjust. We could also add epicycles upon epicycles upon . . . (and, by Fourier, make things match pretty much as well as we want . . . ). 21 On the other hand, we could even go so far as to step to a new paradigm, and allow our theory to include the earth as one of the moving bodies (no longer distinguishing between celestial and terrestrial), and even allow the paths to be other conic sections, such as ellipses. We might make an orrery like this, with an actual physical crank we can turn: 22 Hmmm. Are we done now? What do we want a theory/model to do for us? It seems to me in various contexts we would like it to 1. Give us a good description of the system 2. Allow us to predict behavior of the system 3. Give us an explanation of the system 4. Allow us to control the system A nice heliocentric theory/model (Keplerian, say) can do a very good job of describing the solar system, and can allow us to make good predictions (or retrodictions) of the behavior, but it doesn't give us much in the way of an explanation of the system. In the next step, things get very interesting. 23 Enter Isaac Newton. In the new paradigm, we allow new entities to exist in our theory/model forces. In particular, gravitational force. The picture looks very similar to what we had before: 24 Note, though, that the new entity "gravitational force," shown as an arrow in the model, does not correspond with any observed entity in the world . . . No one has ever observed the gravitational force (i.e., the force itself). People have observed things falling, and drawn the conclusion that there must be a force that causes the things to fall, but the force is not anything anyone has observed it is an entity in the model. The supposed existence of the force in the world is a conclusion drawn from the model, not through direct observation. Within the paradigm of forces and differential equations, etc., it is easy to imagine that we are seeing the effects of forces, but it is worth remembering that we are "always already" looking at the world through the lenses of our models, and it is all to easy to mistake aspects of the lens for real phenomena in the world . . . 25 One more, to clarify the last observation. In general relativity, an object follows a geodesic within a curved spacetime, with curvature determined by local masses. Note that there is no "force of gravity" in this model . . . 26 On building models: My general policy when building a model is to start with the absolute minimum to get the model off the ground. In order to do this, I have to observe / think about the system long and hard, thinking about what might possibly be irrelevant to the aspects of the system's behavior in which I am interested. (See, for example, my "economics" models . . . ) There must be more to come, but I'll stop writing here for now, and talk instead :-) 27 Theories, models and simulation - exercises 1. What sorts of relations can there be between models and reality? 28 References [1] Fox Keller, Evelyn, Making Sense of Life, Harvard University Press, Cambridge, 2002. To top 29
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UCF - MATH - 2112
Nonlinear Systems(. . . and chaos) a brief introductionTom Carter Computer Science CSU Stanislaus http:/csustan.csustan.edu/~ tom/Lecture-Notes/Nonlinear-Systems/Nonlinear-Systems.pdf November 7, 20111Our general topics:What are nonlinear systems? A
UCF - MATH - 2112
PerspectiveComplex Systems Summer School June, 2006
UCF - MATH - 2112
A Little Probability. . Coding and Information Theory Fall, 2004Tom Carter http:/astarte.csustan.edu/~ tom/ October, 20041Some probability background There are two notions of the probability of an event happening. The two general notions are: 1. A fr
UCF - MATH - 2112
A brief overview of quantum computingor, Can we compute faster in a multiverse?Tom Carterhttp:/cogs.csustan.edu/~ tom/quantum. . .. June, 20011Our general topics: Hilbert space and quantum mechanics Tensor products Quantum bits (qubits) Entangled
UCF - MATH - 5485
AIMS Exercise Set # 1Peter J. Olver1. Determine the form of the single precision floating point arithmetic used in the computers at AIMS. What is the largest number that can be accurately represented? What is the smallest positive number n1 ? The second
UCF - MATH - 5485
AIMS Exercise Set # 2Peter J. Olver1. Explain why the equation e- x = x has a solution on the interval [ 0, 1 ]. Use bisection to find the root to 4 decimal places. Can you prove that there are no other roots? 2. Find 6 3 to 5 decimal places by setting
UCF - MATH - 5485
AIMS Exercise Set # 3Peter J. Olver1. Which of the following matrices are regular? If reguolar, write down its L U 1 -2 3 2 1 0 -1 factorization. (a) , (b) , (c) -2 4 -1 . 1 4 3 -2 3 -1 2 2. In each of the following problems, find the A = L U factorizat
UCF - MATH - 5485
AIMS Exercise Set # 4Peter J. Olver1. Find the explicit formula for the solution to the following linear iterative system: u(k+1) = u(k) - 2 v (k) , v (k+1) = - 2 u(k) + v (k) , u(0) = 1, v (0) = 0.2. Determine whether or not the following matrices are
UCF - MATH - 5485
AIMS Exercise Set # 5Peter J. Olver1. Use the power method to find the dominant eigenvalue and associated 4 1 0 1 -2 0 1 1 4 1 0 eigenvector of the following matrices: (a) -3 -2 0 , (b) . 0 1 4 1 -2 5 4 1 0 1 4 2. Use Newton's Method to find all points
UCF - MATH - 5485
AIMS Exercise Set # 6Peter J. Olver1. Prove that the Midpoint Method (10.58) is a second order method. 2. Consider the initial value problem du = u(1 - u), dt for the logistic differential equation. solution for t > 0. (b) Use the Euler Method with step
UCF - MATH - 5485
AIMS Exercise Set # 7Peter J. Olver1. In this exercise, you are asked to find "one-sided" finite difference formulas for derivatives. These are useful for approximating derivatives of functions at or near the boundary of their domain. (a) Construct a se
UCF - MATH - 5485
AIMS Lecture Notes 2006Peter J. Olver6. Eigenvalues and Singular ValuesIn this section, we collect together the basic facts about eigenvalues and eigenvectors. From a geometrical viewpoint, the eigenvectors indicate the directions of pure stretch and t
UCF - MATH - 5485
Chapter 16 Complex AnalysisThe term "complex analysis" refers to the calculus of complex-valued functions f (z) depending on a single complex variable z. On the surface, it may seem that this subject should merely be a simple reworking of standard real v
UCF - MATH - 5485
AIMS Lecture Notes 2006Peter J. Olver1. Computer ArithmeticThe purpose of computing is insight, not numbers. - R.W. Hamming, [23]The main goal of numerical analysis is to develop efficient algorithms for computing precise numerical values of mathemati
UCF - MATH - 5485
Chapter 21 The Calculus of VariationsWe have already had ample opportunity to exploit Nature's propensity to minimize. Minimization principles form one of the most wide-ranging means of formulating mathematical models governing the equilibrium configurat
UCF - MATH - 5485
AIMS Lecture Notes 2006Peter J. Olver3. Review of Matrix AlgebraVectors and matrices are essential for modern analysis of systems of equations - algebrai, differential, functional, etc. In this part, we will review the most basic facts of matrix arithm
UCF - MATH - 5485
Chapter 13 Fourier AnalysisIn addition to their inestimable importance in mathematics and its applications, Fourier series also serve as the entry point into the wonderful world of Fourier analysis and its wide-ranging extensions and generalizations. An
UCF - MATH - 5485
Chapter 12 Fourier SeriesJust before 1800, the French mathematician/physicist/engineer Jean Baptiste Joseph Fourier made an astonishing discovery. As a result of his investigations into the partial differential equations modeling vibration and heat propa
UCF - MATH - 5485
AIMS Lecture Notes 2006Peter J. Olver4. Gaussian EliminationIn this part, our focus will be on the most basic method for solving linear algebraic systems, known as Gaussian Elimination in honor of one of the all-time mathematical greats - the early nin
UCF - MATH - 5485
Chapter 14 Vibration and Diffusion in OneDimensional MediaIn this chapter, we study the solutions, both analytical and numerical, to the two most important equations of one-dimensional continuum dynamics. The heat equation models the diffusion of thermal
UCF - MATH - 5485
Math 5485 September 11, 2006Homework #1Problems: 1.3 1.4 2.1 1b, 3, 4a,b(single precision only), 9. 1a, 2, 7, 13. 1d, 3, 8, 11, 16a.Due: Wednesday, September 20 Text: B. Bradie, A Friendly Introduction to Numerical Analysis.
UCF - MATH - 5485
Math 5485 September 20, 2006Homework #2Problems: 2.2 2.3 2.4 1d, 5 (only do parts 1 & 3), 13. 1, 5, 7, 11. 1d, 4, 9, 14a.Due: Wednesday, September 27 Text: B. Bradie, A Friendly Introduction to Numerical Analysis.
UCF - MATH - 5485
Math 5485 September 27, 2006Homework #3Problems: 2.5 2.6 1d, 6, 11a. 1, 5, 8.Due: Wednesday, October 4 Text: B. Bradie, A Friendly Introduction to Numerical Analysis.
UCF - MATH - 5485
Math 5485 October 4, 2006Homework #4Problems: 3.1 3.2 3.5 1, 8, 10, 12b. 7a, 14, 18b. 10a, 11.Due: Friday, October 13 Text: B. Bradie, A Friendly Introduction to Numerical Analysis.First Midterm: Wednesday, November 1 Will cover chapters 1, 2, 3. You
UCF - MATH - 5485
Math 5485 October 13, 2006Homework #5Problems: 3.3 3.6 3.7 2a, 3a, b(a), c, 5b, d (also, what is the spectral radius?), 6b, c, 7a, 10. 2,10. 14b, 19.Due: Friday, October 20 Text: B. Bradie, A Friendly Introduction to Numerical Analysis.First Midterm:
UCF - MATH - 5485
Math 5485 October 23, 2006Homework #6Problems: 3.7 3.8 5b, 6. 3a, 9, 11 (for 9), 12 (for 9), 13.Due: Monday, November 6 Text: B. Bradie, A Friendly Introduction to Numerical Analysis.First Midterm: Wednesday, November 1 Will cover sections 1.24, 2.16,
UCF - MATH - 5485
Math 5485 November 15, 2006Homework #7Problems: 3.10 4.1 4.2 4.3 7 (just do Newton's Method), 11b. 2, 11, 14a, 15a. 2, 8, 10. 1b, 5.Due: Monday, November 27 Text: B. Bradie, A Friendly Introduction to Numerical Analysis.
UCF - MATH - 5485
Math 5485 November 27, 2006Homework #8Problems: 4.4 4.5 1ac, 4a, 5b, 8. 6 (ignore the Wilkinson shift), 12 (compare the convergence rate of the direct QR algorithm with that based on tridiagonalization).Due: Monday, December 4 Text: B. Bradie, A Friend
UCF - MATH - 5485
Math 5485 December 4, 2006Homework #9Problems: 5.1 5.2 5.3 5.4 2, 5, 8. 1b, 4, 9, 12. 4, 8, 11. 2, 3, 10 (only uniform and Chebyshev).Due: Wednesday, December 13 Text: B. Bradie, A Friendly Introduction to Numerical Analysis. Second Midterm: Friday, De
UCF - MATH - 5485
Chapter 17 Dynamics of Planar MediaIn this chapter, we continue our ascent of the dimensional ladder for linear systems. In Chapter 6, we embarked on our journey with equilibrium configurations of discrete systems - massspring chains, circuits, and struc
UCF - MATH - 5485
AIMS Lecture Notes 2006Peter J. Olver7. Iterative Methods for Linear SystemsLinear iteration coincides with multiplication by successive powers of a matrix; convergence of the iterates depends on the magnitude of its eigenvalues. We discuss in some det
UCF - MATH - 5485
AIMS Lecture Notes 2006Peter J. Olver5. Inner Products and NormsThe norm of a vector is a measure of its size. Besides the familiar Euclidean norm based on the dot product, there are a number of other important norms that are used in numerical analysis
UCF - MATH - 5485
Chapter 15 The Planar Laplace EquationThe fundamental partial differential equations that govern the equilibrium mechanics of multi-dimensional media are the Laplace equation and its inhomogeneous counterpart, the Poisson equation. The Laplace equation i
UCF - MATH - 5485
Very Basic MATLABPeter J. Olver January, 2009 Matrices: Type your matrix as follows: Use space or , to separate entries, and ; or return after each row. > A = [4 5 6 -9;5 0 -3 6;7 8 5 0; -1 4 5 1] or > A = [4,5,6,-9;5,0,-3,6;7,8,5,0;-1,4,5,1] or > A = [
UCF - MATH - 5485
AIMS Lecture Notes 2006Peter J. Olver9. Numerical Solution of Algebraic SystemsIn this part, we discuss basic iterative methods for solving systems of algebraic equations. By far the most common is a vector-valued version of Newton's Method, which will
UCF - MATH - 5485
Chapter 19 Nonlinear SystemsNonlinearity is ubiquitous in physical phenomena. Fluid and plasma mechanics, gas dynamics, elasticity, relativity, chemical reactions, combustion, ecology, biomechanics, and many, many other phenomena are all governed by inhe
UCF - MATH - 5485
Chapter 22 Nonlinear Partial Differential EquationsThe ultimate topic to be touched on in this book is the vast and active field of nonlinear partial differential equations. Leaving aside quantum mechanics, which remains to date an inherently linear theo
UCF - MATH - 5485
AIMS Lecture Notes 2006Peter J. Olver11. Numerical Solution of the Heat and Wave EquationsIn this part, we study numerical solution methodss for the two most important equations of one-dimensional continuum dynamics. The heat equation models the diffus
UCF - MATH - 5485
AIMS Lecture Notes 2006Peter J. Olver10. Numerical Solution of Ordinary Differential EquationsThis part is concerned with the numerical solution of initial value problems for systems of ordinary differential equations. We will introduce the most basic
UCF - MATH - 5485
AIMS Lecture Notes 2006Peter J. Olver8. Numerical Computation of EigenvaluesIn this part, we discuss some practical methods for computing eigenvalues and eigenvectors of matrices. Needless to say, we completely avoid trying to solve (or even write down
UCF - MATH - 5485
AIMS Lecture Notes 2006Peter J. Olver13. Approximation and InterpolationWe will now apply our minimization results to the interpolation and least squares fitting of data and functions.13.1. Least Squares.Linear systems with more equations than unknow
UCF - MATH - 5485
AIMS Lecture Notes 2006Peter J. Olver2. Numerical Solution of Scalar EquationsMost numerical solution methods are based on some form of iteration. The basic idea is that repeated application of the algorithm will produce closer and closer approximation
UCF - MATH - 5485
Chapter 20 Nonlinear Ordinary Differential EquationsThis chapter is concerned with initial value problems for systems of ordinary differential equations. We have already dealt with the linear case in Chapter 9, and so here our emphasis will be on nonline
UCF - MATH - 5485
Chapter 18 Partial Differential Equations in ThreeDimensional SpaceAt last we have ascended the dimensional ladder to its ultimate rung (at least for those of us living in a three-dimensional universe): partial differential equations in physical space. A
UCF - MATH - 5485
Orthogonal Bases and the QR Algorithmby Peter J. Olver University of Minnesota1. Orthogonal Bases.Throughout, we work in the Euclidean vector space V = R n , the space of column vectors with n real entries. As inner product, we will only use the dot pr
UCF - MATH - 5485
AIMS Lecture Notes 2006Peter J. Olver14. Finite ElementsIn this part, we introduce the powerful finite element method for finding numerical approximations to the solutions to boundary value problems involving both ordinary and partial differential equa
UCF - MATH - 5485
AIMS Lecture Notes 2006Peter J. Olver12. MinimizationIn this part, we will introduce and solve the most basic mathematical optimization problem: minimize a quadratic function depending on several variables. This will require a short introduction to pos
UCF - MATH - 5587
Remark : On a connected domain R 2 , all harmonic conjugates to a given function u(x, y) only differ by a constant: v(x, y) = v(x, y) + c; see Exercise . Although most harmonic functions have harmonic conjugates, unfortunately this is not always the case.
UCF - MATH - 5587
Chapter 7 Complex Analysis and Conformal MappingThe term "complex analysis" refers to the calculus of complex-valued functions f (z) depending on a single complex variable z. To the novice, it may seem that this subject should merely be a simple reworkin
UCF - MATH - 5587
1 Re z Figure 7.1.1 Im z 1 Real and Imaginary Parts of f (z) = z .Therefore, if f (z) is any complex function, we can write it as a complex combination f (z) = f (x + i y) = u(x, y) + i v(x, y), of two inter-related real harmonic functions: u(x, y) = Re
UCF - MATH - 5587
Figure 7.4.Real and Imaginary Parts ofz.also have complex logarithms! On the other hand, if z = x < 0 is real and negative, then log z = log | x | + (2 k + 1) i is complex no matter which value of ph z is chosen. (This explains why one avoids defining
UCF - MATH - 5587
The proof of the converse - that any function whose real and imaginary components satisfy the CauchyRiemann equations is differentiable - will be omitted, but can be found in any basic text on complex analysis, e.g., [3, 65, 118]. Remark : It is worth poi
UCF - MATH - 5587
is analytic everywhere except for singularities at the points z = 3 and z = -1, where its denominator vanishes. Since f (z) = h1 (z) , z-3 where h1 (z) = ez (z + 1)21 is analytic at z = 3 and h1 (3) = 16 e3 = 0, we conclude that z = 3 is a simple (order
UCF - MATH - 5587
if and only if it has vanishing divergence: v = u v + = 0. x y (7.36)Incompressibility means that the fluid volume does not change as it flows. Most liquids, including water, are, for all practical purposes, incompressible. On the other hand, the flow is
UCF - MATH - 5587
Using formula (7.19) for the complex derivative, d = -i = u - i v, dz x y so = u, x = v. yThus, = v, and hence the real part (x, y) of the complex function (z) defines a velocity potential for the fluid flow. For this reason, the anti-derivative (z) is k
UCF - MATH - 5587
gDFigure 7.14.Mapping to the Unit Disk.Remark : In this section, we have focused on the fluid mechanical roles of a harmonic function and its conjugate. An analogous interpretation applies when (x, y) represents an electromagnetic potential function;
UCF - MATH - 5587
Figure 7.16.The Effect of = z 2 on Various Domains.obtained by cutting the complex plane along the negative real axis. On the other hand, vertical lines Re z = a are mapped to circles | | = ea . Thus, a vertical strip a < Re z < b is mapped to an annulu
UCF - MATH - 5587
zph zFigure 7.18.Complex Curve and Tangent.notation x(t) = ( x(t), y(t) ) to complex notation z(t) = x(t)+ i y(t). All the usual vectorial curve terminology - closed, simple (non-self intersecting), piecewise smooth, etc. - is employed without modific
UCF - MATH - 5587
Center: .1 Radius: .5Center: .2 + i Radius: 1Center: 1 + i Radius: 1Center: -2 + 3 i Radius: 3 2 4.2426Center: .2 + i Radius: 1.2806 Figure 7.21.Center: .1 + .3 i Radius: .9487Center: .1 + .1 i Radius: 1.1045Center: -.2 + .1 i Radius: 1.2042Airfoi
UCF - MATH - 5587
Example 7.35. The goal of this example is to construct an conformal map that takes a half disk D+ = | z | < 1, Im z > 0 (7.73) to the full unit disk D = cfw_ | | < 1 . The answer is not = z 2 because the image of D+ omits the positive real axis, resulting
UCF - MATH - 5587
7.5. Applications of Conformal Mapping.Let us now apply what we have learned about analytic/conformal maps. We begin with boundary value problems for the Laplace equation, and then present some applications in fluid mechanics. We conclude by discussing h