# An Introduction to Partial Differential Equations

Save this PDF as:
Size: px
Start display at page:

## Transcription

1 An Introduction to Partial Differential Equations Andrew J. Bernoff LECTURE 2 Cooling of a Hot Bar: The Diffusion Equation 2.1. Outline of Lecture An Introduction to Heat Flow Derivation of the Diffusion Equation Examples of Solution to the Diffusion Equation The Maximum Principle Energy Dissipation and Uniqueness 2.2. An Introduction to Heat Flow A classical example of the application of ordinary differential equations is Newton s Law of Cooling which, basically, answers the question How does a cup of coffee cool? Newton hypothesized that the rate at which the temperature, U(t), changes is proportional to the difference with the ambient temperature, which we call Ū, (2.1) du dt = κ(u Ū). Here κ is a positive rate constant (with units of inverse time) that measures how fast heat is lost from the coffee cup to the ambient environment. If we specify the initial temperature, (2.2) U(0) = U 0, 1

2 2 ANDREW J. BERNOFF, AN INTRODUCTION TO PDE we can solve for the evolution of the temperature, (2.3) U(t) = Ū + (U 0 Ū)e κt. Figure 2.1: (a) A coffee cup (b) Its temperature as a function of time. (draw your own figure). If we graph the temperature as a function of time, we see that it decays exponentially to the ambient temperature, Ū, at a rate governed by κ. When we derived Newton s Law of cooling we made several assumptions most importantly that the temperature in the coffee cup did not vary with location. If we account for the variation of temperature with location, we can derive a PDE called the heat equation or, more generally, the diffusion equation. If the temperature, U(x, t) is a function of a single spatial variable, x, we will show that it satisfies the diffusion equation, U t = DU xx, where D is a constant known as the thermal diffusivity. In higher dimensions, the equation can be written where 2 is the Laplacian. U t = D 2 U, 2.3. Derivation of the Diffusion Equation The diffusion equation will be our second example of a conservation law; we can derive the equation by accounting for the flow of thermal energy. Suppose we consider a metal bar, with a uniform cross-sectional area, A, whose temperature, U(x, t), is a function of time, t, and the

3 LECTURE 2. COOLING OF A HOT BAR:THE DIFFUSION EQUATION 3 position, x, along the bar (that is we assume the temperature is uniform in every cross-section). Figure 2.2: Conservation of heat in a metal bar of cross-sectional area A. (draw your own figure). Let the thermal energy in the region a < x < b is given by (2.4) E = ρ 0 c v A b a U(x, t) dx The important term in the integral is the temperature, U(x, t), measured in degrees. The remaining constants, A, the cross-sectional area (with units of [(length) 2 ]), ρ 0, the density [mass/(length) 3 ] and c v, the heat capacity [energy/(degree mass)] are physical properties of the material think of them as being obligatory for making the units work out. We wish to equate the change in thermal energy to the heat flux out of the bar through the planes at x = a and x = b. To do this we use Fourier s heat law which states that the flux density of thermal energy, q(x, t) is proportional to the temperature gradient, (2.5) q(x, t) = ku x, where the negative sign reflects the fact that heat flows from hot to cold, just as in Newton s law of cooling, with a constant of proportionality, k, called the thermal conductivity [(energy length)/(degrees time)].

4 4 ANDREW J. BERNOFF, AN INTRODUCTION TO PDE Figure 2.3: Heat flux is from hot to cold!! (draw your own figure). Now, the total flux of thermal energy into the into the region a < x < b is given by (2.6) Q = A[q(a, t) q(b, t)], where we multiply by the area A to get the total flux through the cross-section. By conservation of energy, the rate of change of the energy between a and b is given by the flux into the region, (2.7) de dt = Q. Once again we can rewrite the flux by a clever application of the fundamental theorem of calculus, (2.8) (2.9) Q = A[q(a, t) q(b, t)] = Aq(x, t) x=b = A b a q x dx. x=a We now rewrite the conservation of energy equation as (2.10) de dt = d [ b ] b b ρc v A U dx = ρc v AU t dx = Q = A dt a a a q x dx,

5 LECTURE 2. COOLING OF A HOT BAR:THE DIFFUSION EQUATION 5 or, rearranging (2.11) b a ρc v A U t + Aq x dx = 0. Since this is true for every interval a < x < b, the integrand must vanish identically. So (2.12) ρc v AU t + Aq x = 0. Substituting for the flux function q(x, t) = ku x yields (2.13) ρc v AU t ka(u x ) x = 0. Rearranging the equation yields the diffusion equation, (2.14) U t = DU xx, where the diffusivity, D = k/(ρc v ), is a constant which is determined by the geometry and physical properties of the metal bar. To complete the description of the problem, we need to supplement the diffusion equation with boundary conditions and initial conditions. Suppose we consider a bar of finite length L, occupying the region 0 < x < L. At the boundaries of the metal bar we can specify a fixed temperature, (2.15) U(0, t) = U 0 U(L, t) = U 1, which are usually referred to as Dirichlet boundary conditions. Alternatively, we could specify a heat flux, (2.16) q 0 = q(0, t) = ku x (0, t) q 1 = q(l, t) = ku x (L, t). Specifying the gradient across the boundary is referred to as Neumann boundary conditions. Finally, we also need to specify the initial temperature distribution, (2.17) U(x, 0) = f(x) 0 < x < L. We will demonstrate below that the solution to this problem (if it exists) is unique; later in this course we will solve this problem using the method of separation variables. For completeness, we also comment here that the problem can be posed on the infinite line, < x < sometime called the Cauchy problem in this case one usually replaces the boundary condition with the specification that the temperature remains bounded as we approach infinity, (2.18) lim U(x, t) < C, x ±

6 6 ANDREW J. BERNOFF, AN INTRODUCTION TO PDE for some constant C. This condition may seem superfluous at first glance, but actually is necessary to stop heat from leaking in from infinity (speaking very, very informally an infinite source of heat infinitely far away can have a finite effect in a short amount of time). If you are interested in details, look for the examples of Tychonov in a PDE s text Examples of Solution to the Diffusion Equation We can summarize the last section by restating a well-posed problem for the diffusion equation on the interval 0 < x < L with Dirichlet boundary conditions, The Dirichlet Problem for the Diffusion Equation (Non-homogeneous Boundary Conditions) U t = DU xx 0 < x < L, t > 0 DE U(0, t) = U 0 U(L, t) = U 1 t > 0 BC U(x, 0) = f(x) 0 < x < L. IC Solving the general problem will have to wait, but we can find some specific solutions to the problem using the ideas of Separation of Variables. For the moment, we will restrict ourselves to homogeneous boundary conditions, The Dirichlet Problem for the Diffusion Equation (Homogeneous Boundary Conditions) U t = DU xx 0 < x < L, t > 0 DE U(0, t) = 0 U(L, t) = 0 t > 0 BC U(x, 0) = f(x) 0 < x < L, IC If you want, you can skip the derivation for the moment and jump ahead to Exercise 1, if you don t mind the solution appearing deus ex machina ( a fancy term for out of thin air ). 1 See, for example, T. W. Körner, Fourier Analysis, Cambridge University Press, p. 338.

7 LECTURE 2. COOLING OF A HOT BAR:THE DIFFUSION EQUATION A Solution to the Homogeneous Dirichlet Problem Let us look for solutions to the homogeneous Dirichlet problem of the form (2.19) U(x, t) = X(x)T (t) we find from the differential equation (DE) that (2.20) XT t = DX xx T and dividing by XT we find T t (2.21) DT = X xx X = λ. where λ is to be determined. Now because T t /DT is only a function of t and X xx /X is only a function of x we know that λ must be independent of x and t respectively, and therefore must be a constant consequently it is known as the separation constant. We can now solve the resulting ODE for T (t) (2.22) T t = λdt T (t) = e λt, or some constant multiple of it. We now look for a solution for the X(x) equation that also satisfies the homogeneous boundary conditions. From the boundary conditions (BC), we know that (2.23) (2.24) U(0, t) = X(0)T (t) = 0 X(0) = 0 U(0, t) = X(L)T (t) = 0 X(L) = 0 So finally we conclude that we are looking for solutions to the Boundary Value Problem for X(x), (2.25) X xx + λx = 0, X(0) = 0 X(L) = 0. Solving the DE, we find that (2.26) X(x) = B cos( λx) + C sin( λx) and applying the boundary conditions we see that X(0) = 0 implies that B = 0, and that (2.27) C sin( λl) = 0. Consequently, a non-trivial solution (that is a solution for which X(x) 0) for X(x) can be found if and only if ( nπ ) 2 (2.28) λ = λ n for n = 1, 2, 3... L

8 8 ANDREW J. BERNOFF, AN INTRODUCTION TO PDE for which we find (2.29) X(x) = X n (x) sin ( nπx ) L for n = 1, 2, 3..., or some constant multiple of it. These special values of λ are called eigenvalues and the associated functions, X n (x), are known as eigenfunctions. Multiplying the solution for X n (x) and T (t) together finally yields a solution for U n (x, t), ( nπx ) nπ (2.30) U(x, t) = U n (x, t) sin e ( L ) 2 Dt for n = 1, 2, L The method of separation of variables is very powerful it will be one of our primary tools for finding solutions to PDE s in the coming lectures. Exercise 1. Verify that ( nπx ) nπ U n (x, t) sin e ( L ) 2 t for n = 1, 2, 3..., L satisfies the diffusion equation U t = DU xx and the homogeneous boundary conditions U(0, t) = U(L, t) = 0. Explain why any linear combination of U n, ( nπx ) nπ U(x, t) = a n U n (x, t) = a n sin e ( L ) 2 Dt L n=1 also satisfies the diffusion equation and the homogeneous boundary condition. Does it worry you that this is an infinite sum? What initial condition, U(x, 0), does this correspond to? n=1

9 LECTURE 2. COOLING OF A HOT BAR:THE DIFFUSION EQUATION A Solution to the Cauchy Problem We can also consider a solution to the Cauchy problem for the diffusion equation, which you hopefully remember is the problem posed on the entire real line, The Cauchy Problem for the Diffusion Equation U t = DU xx < x <, t > 0 DE lim U(x, t) < C x ± t > 0, BC U(x, 0) = f(x) < x <. IC While there are many clever derivation for the solution to this problem, for the moment I will simply give you the most important solution, usually called the fundamental solution or the diffusion kernel, 1 (2.31) U(x, t) = G(x, t + τ) e x2 4πD(t + τ) 4D(t+τ). where τ is a constant (which we will assume is positive). This solution can be used to construct a general solution of the diffusion equation for an arbitrary initial condition, f(x). Exercise 2. Verify that G(x, t + τ) 1 e 4πD(t + τ) x2 4D(t+τ). satisfies the diffusion equation and the boundary conditions for the Cauchy problem when τ > 0. Show that this solution corresponds to a Gaussian with time varying width and height. How does the Gaussian s width, height and area vary in time?

10 10 ANDREW J. BERNOFF, AN INTRODUCTION TO PDE 2.5. The Maximum Principle Looking at solutions to the heat equation, we note that they tend to average out maximums and minimums. We can develop some intuition for this by considering what the equation says. Basically, U t = DU xx means: The temperature is decreasing when the profile is convex down and the temperature is increasing when the profile is convex up. Figure 2.4: The heat equation interperted graphically (draw your own figure). From which we conclude that interior maximums in temperature are decreasing and interior minimums of temperature are increasing. This reasoning is not quite airtight (how to make it tighter is a good question to ponder). We can give a rigorous statement (without proof) of the maximum principle: Theorem 2.32 (Maximum Principle for the Diffusion Equation). If u(x, t) satisfies the Dirichlet problem for the diffusion equation in the semi-infinite strip 0 < x < L, 0 < t, then it assumes its maximum value (as a function of x and t) either initially (when t = 0) or on the lateral boundaries (where x = 0 or x = l). The same is also true of the minimum of u(x, t). A proof can be found in most advanced PDE texts. Exercise 3. Interpret the solutions we have found for the diffusion equation in terms of the maximum principle. Show examples where the maximum value of u(x, t) occur in the initial condition and on the lateral boundaries.

11 LECTURE 2. COOLING OF A HOT BAR:THE DIFFUSION EQUATION Energy Dissipation and Uniqueness By looking at what is normally known as energy for the diffusion equation, we can show that the solution for the Dirichlet problem is unique. Note this energy is a mathematical construct, not to be confused with the thermal energy discussed in the derivation of the diffusion equation. First, suppose that U(x, t) is a solution to the homogeneous Dirichlet problem, U t = DU xx 0 < x < L, t > 0 DE U(0, t) = 0 U(L, t) = 0 t > 0 BC U(x, 0) = f(x) 0 < x < L, IC Let s define, the energy, (2.33) W = 1 2 L 0 U 2 dx, which is a function of t dependent on the particular solution U(x, t) (technically it is a function of t and a functional of U(x, t)). Note that W 0 with W = 0 only for the trivial solution U(x, t) = 0. If we differentiate the energy with respect to time, we find dw dt = L = D = 0 L 0 L 0 UU t dx, UU xx dx, (U x ) 2 dx + UU xx x=l x=0, where we have substituted the DE and used integration by parts. Now, applying the BC s, we fine that the boundary terms from the integration by parts vanish, so, dw dt L = (U x ) 2 dx 0 0 Now, we can conclude that W is decreasing (that is energy is dissipated!!)unless U x = 0, that is to say that U is constant. As the only constant solution satisfying the boundary conditions is U = 0, we might be tempted to conclude that the solution always decays to this trivial state. This turns out to be true, although one must invest some analysis to show it rigorously.

12 12 ANDREW J. BERNOFF, AN INTRODUCTION TO PDE Figure 2.4: (a) A solution to the homogeneous Dirichlet problem for the heat equation. (b) The corresponding energy, W, which is decreasing to zero. (draw your own figure). A second conclusion one can reach is that if f(x) = 0, that U(x, t) = 0 for all t > 0. This follows quickly because W = 0 at t = 0, it is nonincreasing and non-negative. While this seems like a trivial result, it has a very powerful consequence. Suppose we had two solutions to the non-homogeneous Dirichlet problem, call them V 1 and V 2. You should be able to convince yourself that there difference U = V 1 V 2 satisfies the homogeneous Dirichlet problem with f(x) = 0. Consequently, we know that U(x, t) = 0 for all t > 0, which implies V 1 = V 2. From this we conclude that The solution to the non-homogeneous Dirichlet problem is unique, a powerful result indeed. Exercise 4. Convince yourself the energy argument for uniqueness of solutions in the previous paragraph is correct. Show that a similar argument can be made for the Neumann problem.

13 LECTURE 2. COOLING OF A HOT BAR:THE DIFFUSION EQUATION Challenge Problems for Lecture 2 Problem 1. Consider the diffusion equation with homogeneous Neumann boundary conditions. U t = DU xx 0 < x < L, t > 0, DE U x (0, t) = 0 U x (L, t) = 0 t > 0, BC U(x, 0) = f(x) 0 < x < L. IC (a) Explain physically why this corresponds to the diffusion of heat in a metal bar with insulated ends. Make sure you understand what each of the equations corresponds to. (b) Show that (i) U 0 (x, t) = 1 ( nπx ) nπ (ii) U n (x, t) = cos e ( L ) 2 Dt n = 1, 2, 3,... L satisfy both the diffusion equation (DE) and the homogeneous Neumann boundary conditions (BC). (c) Write down a general solution as a linear combination of the solutions you found in part (b). What does this say about f(x) if we assume that this solution also satisfies the initial condition (IC)? Problem 2. In this problem, we will argue that for the homogeneous Neumann problem discussed in Problem 1, that the solution approaches a constant temperature, given by the average of the initial temperature. (a) Suppose we define the total heat energy in the bar as Q(t) = L 0 U(x, t) dx. Show that Q is conserved, that is that it is independent of time (Hint: compute dq ). dt (b) Use the initial condition to compute Q in terms of f(x). (c) Modify the energy argument in the previous section show that the energy is decreasing unless U(x, t) is constant. Use this to argue that U(x, t) approaches a constant solution as t. (d) Finally, use parts (a) and (b) of the problem to show that there is only one possible constant solution for U that is consistent with the conservation of Q. Show that solution corresponds to the bar approaching the average temperature of the initial condition.

14 14 ANDREW J. BERNOFF, AN INTRODUCTION TO PDE Problem 3. Previously we showed that the diffusion kernel, 1 U(x, t) = G(x, t + τ) e x2 4D(t+τ), 4πD(t + τ) satisfies the diffusion equation with an initial condition (a) Show that the total heat, U(x, 0) = G(x, τ). Q(t) = U(x, t) dx, is conserved, and in fact Q(t) = 1, for the heat kernel. (b) Show that as τ 0, that G(x, τ) 0 for x 0 and that G(0, τ). (c) Explain why the solution G(x, t) (i.e. with τ = 0) corresponds to introducing a unit amount of heat concentrated at the origin when t = 0. This is called a δ-function initial condition. Problem 4. A curious property of the diffusion equation is that both derivatives and integrals of solution satisfy the diffusion equation also. This can be used to generate new solutions from existing ones. (a) Show that if U(x, t) satisfies the diffusion equation then ψ(x, t) = U x satisfies the diffusion equation also (Hint: differentiate both sides of the diffusion equation with respect to x). (b) Generate a new solution for the heat equation by differentiating the heat kernel with respect to x. Graph this solution (MAPLE may be useful here) what does it look like? (c) Show that if ψ(x, t) satisfies the heat equation, then so does U(x, t) = x x 0 ψ(ξ, t) dξ. (d) Show that U(x, t) = π x/ 0 4Dt e z2 satisfies the heat equation, by showing its derivative is the heat kernel. Graph the solution at various times what physical problem does this solution correspond to? dz

### Second Order Linear Partial Differential Equations. Part I

Second Order Linear Partial Differential Equations Part I Second linear partial differential equations; Separation of Variables; - point boundary value problems; Eigenvalues and Eigenfunctions Introduction

### Parabolic Equations. Chapter 5. Contents. 5.1.2 Well-Posed Initial-Boundary Value Problem. 5.1.3 Time Irreversibility of the Heat Equation

7 5.1 Definitions Properties Chapter 5 Parabolic Equations Note that we require the solution u(, t bounded in R n for all t. In particular we assume that the boundedness of the smooth function u at infinity

### The Heat Equation. Lectures INF2320 p. 1/88

The Heat Equation Lectures INF232 p. 1/88 Lectures INF232 p. 2/88 The Heat Equation We study the heat equation: u t = u xx for x (,1), t >, (1) u(,t) = u(1,t) = for t >, (2) u(x,) = f(x) for x (,1), (3)

### The one dimensional heat equation: Neumann and Robin boundary conditions

The one dimensional heat equation: Neumann and Robin boundary conditions Ryan C. Trinity University Partial Differential Equations February 28, 2012 with Neumann boundary conditions Our goal is to solve:

### MATH 425, PRACTICE FINAL EXAM SOLUTIONS.

MATH 45, PRACTICE FINAL EXAM SOLUTIONS. Exercise. a Is the operator L defined on smooth functions of x, y by L u := u xx + cosu linear? b Does the answer change if we replace the operator L by the operator

### CONSERVATION LAWS. See Figures 2 and 1.

CONSERVATION LAWS 1. Multivariable calculus 1.1. Divergence theorem (of Gauss). This states that the volume integral in of the divergence of the vector-valued function F is equal to the total flux of F

### An Introduction to Partial Differential Equations in the Undergraduate Curriculum

An Introduction to Partial Differential Equations in the Undergraduate Curriculum J. Tolosa & M. Vajiac LECTURE 11 Laplace s Equation in a Disk 11.1. Outline of Lecture The Laplacian in Polar Coordinates

### Heat equation examples

Heat equation examples The Heat equation is discussed in depth in http://tutorial.math.lamar.edu/classes/de/intropde.aspx, starting on page 6. You may recall Newton s Law of Cooling from Calculus. Just

### SECOND-ORDER LINEAR DIFFERENTIAL EQUATIONS

SECOND-ORDER LINEAR DIFFERENTIAL EQUATIONS A second-order linear differential equation has the form 1 Px d y dx dy Qx dx Rxy Gx where P, Q, R, and G are continuous functions. Equations of this type arise

### Application of Fourier Transform to PDE (I) Fourier Sine Transform (application to PDEs defined on a semi-infinite domain)

Application of Fourier Transform to PDE (I) Fourier Sine Transform (application to PDEs defined on a semi-infinite domain) The Fourier Sine Transform pair are F. T. : U = 2/ u x sin x dx, denoted as U

### The two dimensional heat equation

The two dimensional heat equation Ryan C. Trinity University Partial Differential Equations March 6, 2012 Physical motivation Consider a thin rectangular plate made of some thermally conductive material.

### CHAPTER 2. Eigenvalue Problems (EVP s) for ODE s

A SERIES OF CLASS NOTES FOR 005-006 TO INTRODUCE LINEAR AND NONLINEAR PROBLEMS TO ENGINEERS, SCIENTISTS, AND APPLIED MATHEMATICIANS DE CLASS NOTES 4 A COLLECTION OF HANDOUTS ON PARTIAL DIFFERENTIAL EQUATIONS

### Lecture 7: Continuous Random Variables

Lecture 7: Continuous Random Variables 21 September 2005 1 Our First Continuous Random Variable The back of the lecture hall is roughly 10 meters across. Suppose it were exactly 10 meters, and consider

### arxiv:1201.6059v2 [physics.class-ph] 27 Aug 2012

Green s functions for Neumann boundary conditions Jerrold Franklin Department of Physics, Temple University, Philadelphia, PA 19122-682 arxiv:121.659v2 [physics.class-ph] 27 Aug 212 (Dated: August 28,

### LS.6 Solution Matrices

LS.6 Solution Matrices In the literature, solutions to linear systems often are expressed using square matrices rather than vectors. You need to get used to the terminology. As before, we state the definitions

### College of the Holy Cross, Spring 2009 Math 373, Partial Differential Equations Midterm 1 Practice Questions

College of the Holy Cross, Spring 29 Math 373, Partial Differential Equations Midterm 1 Practice Questions 1. (a) Find a solution of u x + u y + u = xy. Hint: Try a polynomial of degree 2. Solution. Use

### Introduction to the Finite Element Method

Introduction to the Finite Element Method 09.06.2009 Outline Motivation Partial Differential Equations (PDEs) Finite Difference Method (FDM) Finite Element Method (FEM) References Motivation Figure: cross

### CBE 6333, R. Levicky 1 Differential Balance Equations

CBE 6333, R. Levicky 1 Differential Balance Equations We have previously derived integral balances for mass, momentum, and energy for a control volume. The control volume was assumed to be some large object,

### 5.4 The Heat Equation and Convection-Diffusion

5.4. THE HEAT EQUATION AND CONVECTION-DIFFUSION c 6 Gilbert Strang 5.4 The Heat Equation and Convection-Diffusion The wave equation conserves energy. The heat equation u t = u xx dissipates energy. The

### Differentiation and Integration

This material is a supplement to Appendix G of Stewart. You should read the appendix, except the last section on complex exponentials, before this material. Differentiation and Integration Suppose we have

### Numerical Methods for Differential Equations

Numerical Methods for Differential Equations Chapter 1: Initial value problems in ODEs Gustaf Söderlind and Carmen Arévalo Numerical Analysis, Lund University Textbooks: A First Course in the Numerical

### 4.5 Linear Dependence and Linear Independence

4.5 Linear Dependence and Linear Independence 267 32. {v 1, v 2 }, where v 1, v 2 are collinear vectors in R 3. 33. Prove that if S and S are subsets of a vector space V such that S is a subset of S, then

### Lecture 3 Fluid Dynamics and Balance Equa6ons for Reac6ng Flows

Lecture 3 Fluid Dynamics and Balance Equa6ons for Reac6ng Flows 3.- 1 Basics: equations of continuum mechanics - balance equations for mass and momentum - balance equations for the energy and the chemical

### 1 Completeness of a Set of Eigenfunctions. Lecturer: Naoki Saito Scribe: Alexander Sheynis/Allen Xue. May 3, 2007. 1.1 The Neumann Boundary Condition

MAT 280: Laplacian Eigenfunctions: Theory, Applications, and Computations Lecture 11: Laplacian Eigenvalue Problems for General Domains III. Completeness of a Set of Eigenfunctions and the Justification

### Math 2280 - Assignment 6

Math 2280 - Assignment 6 Dylan Zwick Spring 2014 Section 3.8-1, 3, 5, 8, 13 Section 4.1-1, 2, 13, 15, 22 Section 4.2-1, 10, 19, 28 1 Section 3.8 - Endpoint Problems and Eigenvalues 3.8.1 For the eigenvalue

### Second Order Linear Nonhomogeneous Differential Equations; Method of Undetermined Coefficients. y + p(t) y + q(t) y = g(t), g(t) 0.

Second Order Linear Nonhomogeneous Differential Equations; Method of Undetermined Coefficients We will now turn our attention to nonhomogeneous second order linear equations, equations with the standard

### Numerical Analysis Lecture Notes

Numerical Analysis Lecture Notes Peter J. Olver. Finite Difference Methods for Partial Differential Equations As you are well aware, most differential equations are much too complicated to be solved by

### The continuous and discrete Fourier transforms

FYSA21 Mathematical Tools in Science The continuous and discrete Fourier transforms Lennart Lindegren Lund Observatory (Department of Astronomy, Lund University) 1 The continuous Fourier transform 1.1

### Module 1 : Conduction. Lecture 5 : 1D conduction example problems. 2D conduction

Module 1 : Conduction Lecture 5 : 1D conduction example problems. 2D conduction Objectives In this class: An example of optimization for insulation thickness is solved. The 1D conduction is considered

### 3. Reaction Diffusion Equations Consider the following ODE model for population growth

3. Reaction Diffusion Equations Consider the following ODE model for population growth u t a u t u t, u 0 u 0 where u t denotes the population size at time t, and a u plays the role of the population dependent

### TOPIC 4: DERIVATIVES

TOPIC 4: DERIVATIVES 1. The derivative of a function. Differentiation rules 1.1. The slope of a curve. The slope of a curve at a point P is a measure of the steepness of the curve. If Q is a point on the

### EXISTENCE AND NON-EXISTENCE RESULTS FOR A NONLINEAR HEAT EQUATION

Sixth Mississippi State Conference on Differential Equations and Computational Simulations, Electronic Journal of Differential Equations, Conference 5 (7), pp. 5 65. ISSN: 7-669. UL: http://ejde.math.txstate.edu

### Walrasian Demand. u(x) where B(p, w) = {x R n + : p x w}.

Walrasian Demand Econ 2100 Fall 2015 Lecture 5, September 16 Outline 1 Walrasian Demand 2 Properties of Walrasian Demand 3 An Optimization Recipe 4 First and Second Order Conditions Definition Walrasian

### EXIT TIME PROBLEMS AND ESCAPE FROM A POTENTIAL WELL

EXIT TIME PROBLEMS AND ESCAPE FROM A POTENTIAL WELL Exit Time problems and Escape from a Potential Well Escape From a Potential Well There are many systems in physics, chemistry and biology that exist

### Chapter 9 Partial Differential Equations

363 One must learn by doing the thing; though you think you know it, you have no certainty until you try. Sophocles (495-406)BCE Chapter 9 Partial Differential Equations A linear second order partial differential

### 1 Finite difference example: 1D implicit heat equation

1 Finite difference example: 1D implicit heat equation 1.1 Boundary conditions Neumann and Dirichlet We solve the transient heat equation ρc p t = ( k ) (1) on the domain L/2 x L/2 subject to the following

### Math 447/547 Partial Differential Equations Prof. Carlson Homework 7 Text section 4.2 1. Solve the diffusion problem. u(t,0) = 0 = u x (t,l).

Math 447/547 Partia Differentia Equations Prof. Carson Homework 7 Text section 4.2 1. Sove the diffusion probem with the mixed boundary conditions u t = ku xx, < x

### 5.3 Improper Integrals Involving Rational and Exponential Functions

Section 5.3 Improper Integrals Involving Rational and Exponential Functions 99.. 3. 4. dθ +a cos θ =, < a

### Lectures 5-6: Taylor Series

Math 1d Instructor: Padraic Bartlett Lectures 5-: Taylor Series Weeks 5- Caltech 213 1 Taylor Polynomials and Series As we saw in week 4, power series are remarkably nice objects to work with. In particular,

### MATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.

MATH10212 Linear Algebra Textbook: D. Poole, Linear Algebra: A Modern Introduction. Thompson, 2006. ISBN 0-534-40596-7. Systems of Linear Equations Definition. An n-dimensional vector is a row or a column

### 3. Diffusion of an Instantaneous Point Source

3. Diffusion of an Instantaneous Point Source The equation of conservation of mass is also known as the transport equation, because it describes the transport of scalar species in a fluid systems. In this

### 1 if 1 x 0 1 if 0 x 1

Chapter 3 Continuity In this chapter we begin by defining the fundamental notion of continuity for real valued functions of a single real variable. When trying to decide whether a given function is or

### Practice with Proofs

Practice with Proofs October 6, 2014 Recall the following Definition 0.1. A function f is increasing if for every x, y in the domain of f, x < y = f(x) < f(y) 1. Prove that h(x) = x 3 is increasing, using

### Numerical Methods for Differential Equations

Numerical Methods for Differential Equations Course objectives and preliminaries Gustaf Söderlind and Carmen Arévalo Numerical Analysis, Lund University Textbooks: A First Course in the Numerical Analysis

### Real Roots of Univariate Polynomials with Real Coefficients

Real Roots of Univariate Polynomials with Real Coefficients mostly written by Christina Hewitt March 22, 2012 1 Introduction Polynomial equations are used throughout mathematics. When solving polynomials

### Scalar Valued Functions of Several Variables; the Gradient Vector

Scalar Valued Functions of Several Variables; the Gradient Vector Scalar Valued Functions vector valued function of n variables: Let us consider a scalar (i.e., numerical, rather than y = φ(x = φ(x 1,

### Solutions to Homework 10

Solutions to Homework 1 Section 7., exercise # 1 (b,d): (b) Compute the value of R f dv, where f(x, y) = y/x and R = [1, 3] [, 4]. Solution: Since f is continuous over R, f is integrable over R. Let x

### A power series about x = a is the series of the form

POWER SERIES AND THE USES OF POWER SERIES Elizabeth Wood Now we are finally going to start working with a topic that uses all of the information from the previous topics. The topic that we are going to

### Notes on Continuous Random Variables

Notes on Continuous Random Variables Continuous random variables are random quantities that are measured on a continuous scale. They can usually take on any value over some interval, which distinguishes

### Metric Spaces. Chapter 7. 7.1. Metrics

Chapter 7 Metric Spaces A metric space is a set X that has a notion of the distance d(x, y) between every pair of points x, y X. The purpose of this chapter is to introduce metric spaces and give some

### 1 The 1-D Heat Equation

The 1-D Heat Equation 18.303 Linear Partial Differential Equations Matthew J. Hancock Fall 006 1 The 1-D Heat Equation 1.1 Physical derivation Reference: Guenther & Lee 1.3-1.4, Myint-U & Debnath.1 and.5

### Pacific Journal of Mathematics

Pacific Journal of Mathematics GLOBAL EXISTENCE AND DECREASING PROPERTY OF BOUNDARY VALUES OF SOLUTIONS TO PARABOLIC EQUATIONS WITH NONLOCAL BOUNDARY CONDITIONS Sangwon Seo Volume 193 No. 1 March 2000

### Microeconomic Theory: Basic Math Concepts

Microeconomic Theory: Basic Math Concepts Matt Van Essen University of Alabama Van Essen (U of A) Basic Math Concepts 1 / 66 Basic Math Concepts In this lecture we will review some basic mathematical concepts

### On computer algebra-aided stability analysis of dierence schemes generated by means of Gr obner bases

On computer algebra-aided stability analysis of dierence schemes generated by means of Gr obner bases Vladimir Gerdt 1 Yuri Blinkov 2 1 Laboratory of Information Technologies Joint Institute for Nuclear

### Chapter 17. Orthogonal Matrices and Symmetries of Space

Chapter 17. Orthogonal Matrices and Symmetries of Space Take a random matrix, say 1 3 A = 4 5 6, 7 8 9 and compare the lengths of e 1 and Ae 1. The vector e 1 has length 1, while Ae 1 = (1, 4, 7) has length

### 19.7. Applications of Differential Equations. Introduction. Prerequisites. Learning Outcomes. Learning Style

Applications of Differential Equations 19.7 Introduction Blocks 19.2 to 19.6 have introduced several techniques for solving commonly-occurring firstorder and second-order ordinary differential equations.

### DIFFERENTIABILITY OF COMPLEX FUNCTIONS. Contents

DIFFERENTIABILITY OF COMPLEX FUNCTIONS Contents 1. Limit definition of a derivative 1 2. Holomorphic functions, the Cauchy-Riemann equations 3 3. Differentiability of real functions 5 4. A sufficient condition

### Reaction diffusion systems and pattern formation

Chapter 5 Reaction diffusion systems and pattern formation 5.1 Reaction diffusion systems from biology In ecological problems, different species interact with each other, and in chemical reactions, different

### Constrained optimization.

ams/econ 11b supplementary notes ucsc Constrained optimization. c 2010, Yonatan Katznelson 1. Constraints In many of the optimization problems that arise in economics, there are restrictions on the values

### Introduction to Algebraic Geometry. Bézout s Theorem and Inflection Points

Introduction to Algebraic Geometry Bézout s Theorem and Inflection Points 1. The resultant. Let K be a field. Then the polynomial ring K[x] is a unique factorisation domain (UFD). Another example of a

### Particular Solution to a Time-Fractional Heat Equation

Particular Solution to a Time-Fractional Heat Equation Simon P. Kelow Kevin M. Hayden (SPK39@nau.edu) (Kevin.Hayden@nau.edu) July 21, 2013 Abstract When the derivative of a function is non-integer order,

### Lecture Notes on Elasticity of Substitution

Lecture Notes on Elasticity of Substitution Ted Bergstrom, UCSB Economics 210A March 3, 2011 Today s featured guest is the elasticity of substitution. Elasticity of a function of a single variable Before

### Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 18. A Brief Introduction to Continuous Probability

CS 7 Discrete Mathematics and Probability Theory Fall 29 Satish Rao, David Tse Note 8 A Brief Introduction to Continuous Probability Up to now we have focused exclusively on discrete probability spaces

### Fourth-Order Compact Schemes of a Heat Conduction Problem with Neumann Boundary Conditions

Fourth-Order Compact Schemes of a Heat Conduction Problem with Neumann Boundary Conditions Jennifer Zhao, 1 Weizhong Dai, Tianchan Niu 1 Department of Mathematics and Statistics, University of Michigan-Dearborn,

### CHAPTER IV - BROWNIAN MOTION

CHAPTER IV - BROWNIAN MOTION JOSEPH G. CONLON 1. Construction of Brownian Motion There are two ways in which the idea of a Markov chain on a discrete state space can be generalized: (1) The discrete time

### Chapter 20. Vector Spaces and Bases

Chapter 20. Vector Spaces and Bases In this course, we have proceeded step-by-step through low-dimensional Linear Algebra. We have looked at lines, planes, hyperplanes, and have seen that there is no limit

### FINAL EXAM SOLUTIONS Math 21a, Spring 03

INAL EXAM SOLUIONS Math 21a, Spring 3 Name: Start by printing your name in the above box and check your section in the box to the left. MW1 Ken Chung MW1 Weiyang Qiu MW11 Oliver Knill h1 Mark Lucianovic

### Let s first see how precession works in quantitative detail. The system is illustrated below: ...

lecture 20 Topics: Precession of tops Nutation Vectors in the body frame The free symmetric top in the body frame Euler s equations The free symmetric top ala Euler s The tennis racket theorem As you know,

### x a x 2 (1 + x 2 ) n.

Limits and continuity Suppose that we have a function f : R R. Let a R. We say that f(x) tends to the limit l as x tends to a; lim f(x) = l ; x a if, given any real number ɛ > 0, there exists a real number

### Maximum Likelihood Estimation

Math 541: Statistical Theory II Lecturer: Songfeng Zheng Maximum Likelihood Estimation 1 Maximum Likelihood Estimation Maximum likelihood is a relatively simple method of constructing an estimator for

### Math 265 (Butler) Practice Midterm II B (Solutions)

Math 265 (Butler) Practice Midterm II B (Solutions) 1. Find (x 0, y 0 ) so that the plane tangent to the surface z f(x, y) x 2 + 3xy y 2 at ( x 0, y 0, f(x 0, y 0 ) ) is parallel to the plane 16x 2y 2z

### Solutions for Review Problems

olutions for Review Problems 1. Let be the triangle with vertices A (,, ), B (4,, 1) and C (,, 1). (a) Find the cosine of the angle BAC at vertex A. (b) Find the area of the triangle ABC. (c) Find a vector

### Derive 5: The Easiest... Just Got Better!

Liverpool John Moores University, 1-15 July 000 Derive 5: The Easiest... Just Got Better! Michel Beaudin École de Technologie Supérieure, Canada Email; mbeaudin@seg.etsmtl.ca 1. Introduction Engineering

### SOLUTIONS TO EXERCISES FOR. MATHEMATICS 205A Part 3. Spaces with special properties

SOLUTIONS TO EXERCISES FOR MATHEMATICS 205A Part 3 Fall 2008 III. Spaces with special properties III.1 : Compact spaces I Problems from Munkres, 26, pp. 170 172 3. Show that a finite union of compact subspaces

### 1 The EOQ and Extensions

IEOR4000: Production Management Lecture 2 Professor Guillermo Gallego September 9, 2004 Lecture Plan 1. The EOQ and Extensions 2. Multi-Item EOQ Model 1 The EOQ and Extensions This section is devoted to

### Eigenvalues, Eigenvectors, and Differential Equations

Eigenvalues, Eigenvectors, and Differential Equations William Cherry April 009 (with a typo correction in November 05) The concepts of eigenvalue and eigenvector occur throughout advanced mathematics They

### Representation of functions as power series

Representation of functions as power series Dr. Philippe B. Laval Kennesaw State University November 9, 008 Abstract This document is a summary of the theory and techniques used to represent functions

### Mechanics 1: Conservation of Energy and Momentum

Mechanics : Conservation of Energy and Momentum If a certain quantity associated with a system does not change in time. We say that it is conserved, and the system possesses a conservation law. Conservation

### 6.4 Logarithmic Equations and Inequalities

6.4 Logarithmic Equations and Inequalities 459 6.4 Logarithmic Equations and Inequalities In Section 6.3 we solved equations and inequalities involving exponential functions using one of two basic strategies.

### Chapter 22: The Electric Field. Read Chapter 22 Do Ch. 22 Questions 3, 5, 7, 9 Do Ch. 22 Problems 5, 19, 24

Chapter : The Electric Field Read Chapter Do Ch. Questions 3, 5, 7, 9 Do Ch. Problems 5, 19, 4 The Electric Field Replaces action-at-a-distance Instead of Q 1 exerting a force directly on Q at a distance,

### Properties of Real Numbers

16 Chapter P Prerequisites P.2 Properties of Real Numbers What you should learn: Identify and use the basic properties of real numbers Develop and use additional properties of real numbers Why you should

### The Point-Slope Form

7. The Point-Slope Form 7. OBJECTIVES 1. Given a point and a slope, find the graph of a line. Given a point and the slope, find the equation of a line. Given two points, find the equation of a line y Slope

### 15 Kuhn -Tucker conditions

5 Kuhn -Tucker conditions Consider a version of the consumer problem in which quasilinear utility x 2 + 4 x 2 is maximised subject to x +x 2 =. Mechanically applying the Lagrange multiplier/common slopes

### 8 Hyperbolic Systems of First-Order Equations

8 Hyperbolic Systems of First-Order Equations Ref: Evans, Sec 73 8 Definitions and Examples Let U : R n (, ) R m Let A i (x, t) beanm m matrix for i,,n Let F : R n (, ) R m Consider the system U t + A

### 6. Define log(z) so that π < I log(z) π. Discuss the identities e log(z) = z and log(e w ) = w.

hapter omplex integration. omplex number quiz. Simplify 3+4i. 2. Simplify 3+4i. 3. Find the cube roots of. 4. Here are some identities for complex conjugate. Which ones need correction? z + w = z + w,

### Integrals of Rational Functions

Integrals of Rational Functions Scott R. Fulton Overview A rational function has the form where p and q are polynomials. For example, r(x) = p(x) q(x) f(x) = x2 3 x 4 + 3, g(t) = t6 + 4t 2 3, 7t 5 + 3t

### or, put slightly differently, the profit maximizing condition is for marginal revenue to equal marginal cost:

Chapter 9 Lecture Notes 1 Economics 35: Intermediate Microeconomics Notes and Sample Questions Chapter 9: Profit Maximization Profit Maximization The basic assumption here is that firms are profit maximizing.

### Unified Lecture # 4 Vectors

Fall 2005 Unified Lecture # 4 Vectors These notes were written by J. Peraire as a review of vectors for Dynamics 16.07. They have been adapted for Unified Engineering by R. Radovitzky. References [1] Feynmann,

### Probability and Statistics Prof. Dr. Somesh Kumar Department of Mathematics Indian Institute of Technology, Kharagpur

Probability and Statistics Prof. Dr. Somesh Kumar Department of Mathematics Indian Institute of Technology, Kharagpur Module No. #01 Lecture No. #15 Special Distributions-VI Today, I am going to introduce

### Math 1B, lecture 5: area and volume

Math B, lecture 5: area and volume Nathan Pflueger 6 September 2 Introduction This lecture and the next will be concerned with the computation of areas of regions in the plane, and volumes of regions in

### MA4001 Engineering Mathematics 1 Lecture 10 Limits and Continuity

MA4001 Engineering Mathematics 1 Lecture 10 Limits and Dr. Sarah Mitchell Autumn 2014 Infinite limits If f(x) grows arbitrarily large as x a we say that f(x) has an infinite limit. Example: f(x) = 1 x

### t := maxγ ν subject to ν {0,1,2,...} and f(x c +γ ν d) f(x c )+cγ ν f (x c ;d).

1. Line Search Methods Let f : R n R be given and suppose that x c is our current best estimate of a solution to P min x R nf(x). A standard method for improving the estimate x c is to choose a direction

### Scientific Programming

1 The wave equation Scientific Programming Wave Equation The wave equation describes how waves propagate: light waves, sound waves, oscillating strings, wave in a pond,... Suppose that the function h(x,t)

### x 2 + y 2 = 1 y 1 = x 2 + 2x y = x 2 + 2x + 1

Implicit Functions Defining Implicit Functions Up until now in this course, we have only talked about functions, which assign to every real number x in their domain exactly one real number f(x). The graphs

### Lecture 3 : The Natural Exponential Function: f(x) = exp(x) = e x. y = exp(x) if and only if x = ln(y)

Lecture 3 : The Natural Exponential Function: f(x) = exp(x) = Last day, we saw that the function f(x) = ln x is one-to-one, with domain (, ) and range (, ). We can conclude that f(x) has an inverse function

### Critical Thresholds in Euler-Poisson Equations. Shlomo Engelberg Jerusalem College of Technology Machon Lev

Critical Thresholds in Euler-Poisson Equations Shlomo Engelberg Jerusalem College of Technology Machon Lev 1 Publication Information This work was performed with Hailiang Liu & Eitan Tadmor. These results

### Vector and Matrix Norms

Chapter 1 Vector and Matrix Norms 11 Vector Spaces Let F be a field (such as the real numbers, R, or complex numbers, C) with elements called scalars A Vector Space, V, over the field F is a non-empty

### Separable First Order Differential Equations

Separable First Order Differential Equations Form of Separable Equations which take the form = gx hy or These are differential equations = gxĥy, where gx is a continuous function of x and hy is a continuously