HOMEWORK 4 1. P45. # 1.

Size: px
Start display at page:

Download "HOMEWORK 4 1. P45. # 1."

Transcription

1 HOMEWORK 4 SHUANGLIN SHAO P45 # Proof By the maximum principle, u(x, t x kt attains the maximum at the bottom or on the two sides When t, x kt x attains the maximum at x, ie, x When x, x kt kt attains the maximum in the interval t T When x, x kt kt attains the maximum in the interval t T Thus the maximum of u is in the closed rectangle { x, t T } P45 # Proof (a Let M(T the maximum of u(x, t in the closed rectangle { x l, t T } M(T is a decreasing function of T Indeed, when T T, the rectangle R { x l, t T } is contained in the rectangle R { x l, t T }; the bottom and the two lateral sides of R are contained in those of R On the other hand, the uniqueness of solutions to the diffusion equation shows that the solution u on R is an extension of u on R So by the maximum principle, M(T M(T (b Similarly as in proving (a, m(t is an increasing function of T 3 P45 #3 Proof (a By the strong maximum principle, u(x, t > in the interior points < x <, < t < because the minimum value of u,, is attained at the boundary point, and at the two lateral sides

2 (b We follow the hint Let µ(t the maximum of u(x, t over x Let X(t [, ] such that µ(t u(x(t, t On the two lateral sides, the value of u is By part (a, µ(t > So X(t (, On the point (X(t, t, following the proof of the maximum principle in the book, So we differentiate µ in t, u x (X(t, t, u t (X(t, t µ (t u x X (t + u t u t We can establish it by a different method For each t >, let µ(t the maximum of u(x, t over x By part (a, u(x, t for all x, t Then µ(t for t > Suppose that < t < t Define R(t, t { x, t t t } Since the value of u is on the two lateral sides, by the maximum principle, the maximum of u on R(t, t is µ(t This implies that µ(t µ(t So µ is decreasing in t > 4 P46 # 4 Proof (a On the two lateral sides and on the bottom, the minimum and the maximum of u is and 4, respectively So by the strong maximum principle, < u(x, t < (b Both u(x, t and u( x, t satisfy the equation u t ku xx and the two lateral side conditions, and the initial condition By the uniqueness theorem for the diffusion equation, u(x, t u( x, t (c Let E(t (u(x, t dx

3 We differentiate it in t, de(t dt k uu t dx uu xx dx uu x x x k (u x dx k (u x dx So E(t is decreasing in t 5 P46 # 6 Proof We prove it by considering the difference w v u The function w(x, t satisfies the equation w t kw xx, with w being nonnegative when either t, orx or x l By the maximum principle, the minimum value of w is attained when either t, or x or x l So Thus for t < and x l w u v 6 P5 # Proof The function φ satisfies that So the solution u is u(x, t φ(x, x < l; φ(x, x > l l l l e (x y φ(ydy e (x y dy l e (x y dy 3

4 For the first integral, l l x e ( y x dy l x π l x Erf( Similarly for the second integral, e z dz e z dz Hence l l+x e ( y x dy l+x π l+x Erf( u(x, t e z dz e z dz Erf( l x l+x Erf( 7 P5 # Proof The function φ satisfies that So the solution u is u(x, t φ(x, for x > ; φ(x 3, for x < e (x y φ(ydy e (x y dy + 3 e (x y dy 4

5 For the first integral, π Erf( x x e ( x y dy e z ( dz x e z dz Similarly for the second integral, π e ( y x dy x x 3Erf( x e z dz e z dz Hence u(x, t Erf( x + 3Erf( x 5

6 8 P5 # 3 Proof u(x, t e3x 9kt e3x 9kt π e 3x 9kt e (x y φ(ydy e (x y +3y dy e x xy+y +kyt dy e (y x+6kt +kxt 36k t dy e (y x+6kt +3x 9kt dy ( e y x+6kt dy e y dy 9 P5 # 6 Proof Let A e x dx e x dx Thus A e x y dxdy 4 e (x +y dxdy 4 R 4 π 4 π 4 π ( 4 e r πrdr e r d(r e x dx π 4 6

7 So A π P5 #7 Proof From Exercise # 6, If setting p x, we have e p dp e x dx π which implies that π e x dx π S(x, tdx, S(x, tdx P53 # 8 Proof Let Since S is even function in x, S(x, t πkt e x S(x, t S( x, t Thus S(x, t S( x, t max S(x, t max S(x, t δ x < δ x< For each t >, the function S(x, t is decreasing on the interval [δ, Hence max S(x, t δ x< πkt e δ 7

8 We will apply the L Hospital rule to show that e δ / goes to zero as t goes to zero πk lim t / t e δ πk lim t δ t/ e δ k which goes to zero as both t / and e δ This proves the claim /t 3/ e δ δ go to zero when t goes to zero P53 # 5 Proof Let u, v be two solutions to the diffusion equation with Neumann boundary conditions Let w u v Then w satisfies the following system of equations: w t kw xx w(x,, w x (, t, w x (l, t We multiply the equation by w to obtain w(w t kw xx Thus d(w k(w x w x + kwx dt Integrating both sides and using the boundary conditions, we see that d w dx + k w dt xdx Since k, we see that the quantity w dx is decreasing for t > Thus (u(x, t v(x, t dx w (x, dx This implies that u(x, t v(x, t for all x, t This proves the uniqueness 8

9 3 P 54 # 6 Proof We set u(x, t e bt v(x, t We substitute u into the equation u t ku xx + bu e bt ( bv + v t kv xx + bv Thus v t kv xx The initial condition changes to v(x, φ(x formula for the homogeneous diffusion equation, where v(x, t S(x, t S(x y, tφ(ydy, e x Hence by the solution Therefore u(x, t e bt S(x y, tφ(ydy This is the solution Department of Mathematics, KU, Lawrence, KS address: slshao@mathkuedu 9

HOMEWORK 5. Proof. This is the diffusion equation (1) with the function φ(x) = e x. By the solution formula (6), 1. e (x y)2.

HOMEWORK 5. Proof. This is the diffusion equation (1) with the function φ(x) = e x. By the solution formula (6), 1. e (x y)2. HOMEWORK 5 SHUANGLIN SHAO. Section 3.. #. Proof. This is the diffusion equation with the function φx e x. By the solution formula 6, vx, t e x y e x+y φydy e x y e x+y e x y y dy e y dy e x+y y dy To compute

More information

Diffusion on the half-line. The Dirichlet problem

Diffusion on the half-line. The Dirichlet problem Diffusion on the half-line The Dirichlet problem Consider the initial boundary value problem (IBVP) on the half line (, ): v t kv xx = v(x, ) = φ(x) v(, t) =. The solution will be obtained by the reflection

More information

PDEs, Homework #3 Solutions. 1. Use Hölder s inequality to show that the solution of the heat equation

PDEs, Homework #3 Solutions. 1. Use Hölder s inequality to show that the solution of the heat equation PDEs, Homework #3 Solutions. Use Hölder s inequality to show that the solution of the heat equation u t = ku xx, u(x, = φ(x (HE goes to zero as t, if φ is continuous and bounded with φ L p for some p.

More information

Partial Differential Equations, Winter 2015

Partial Differential Equations, Winter 2015 Partial Differential Equations, Winter 215 Homework #2 Due: Thursday, February 12th, 215 1. (Chapter 2.1) Solve u xx + u xt 2u tt =, u(x, ) = φ(x), u t (x, ) = ψ(x). Hint: Factor the operator as we did

More information

MATH 425, HOMEWORK 3 SOLUTIONS

MATH 425, HOMEWORK 3 SOLUTIONS MATH 425, HOMEWORK 3 SOLUTIONS Exercise. (The differentiation property of the heat equation In this exercise, we will use the fact that the derivative of a solution to the heat equation again solves the

More information

MATH 425, HOMEWORK 5, SOLUTIONS

MATH 425, HOMEWORK 5, SOLUTIONS MATH 425, HOMEWORK 5, SOLUTIONS Exercise (Uniqueness for the heat equation on R) Suppose that the functions u, u 2 : R x R t R solve: t u k 2 xu = 0, x R, t > 0 u (x, 0) = φ(x), x R and t u 2 k 2 xu 2

More information

x ct x + t , and the characteristics for the associated transport equation would be given by the solution of the ode dx dt = 1 4. ξ = x + t 4.

x ct x + t , and the characteristics for the associated transport equation would be given by the solution of the ode dx dt = 1 4. ξ = x + t 4. . The solution is ( 2 e x+ct + e x ct) + 2c x+ct x ct sin(s)dx ( e x+ct + e x ct) + ( cos(x + ct) + cos(x ct)) 2 2c 2. To solve the PDE u xx 3u xt 4u tt =, you can first fact the differential operat to

More information

Heat Equation on Unbounded Intervals

Heat Equation on Unbounded Intervals Heat Equation on Unbounded Intervals MATH 467 Partial Differential Equations J. Robert Buchanan Department of Mathematics Fall 28 Objectives In this lesson we will learn about: the fundamental solution

More information

Diffusion equation in one spatial variable Cauchy problem. u(x, 0) = φ(x)

Diffusion equation in one spatial variable Cauchy problem. u(x, 0) = φ(x) Diffusion equation in one spatial variable Cauchy problem. u t (x, t) k u xx (x, t) = f(x, t), x R, t > u(x, ) = φ(x) 1 Some more mathematics { if x < Θ(x) = 1 if x > is the Heaviside step function. It

More information

LECTURE NOTES FOR MATH 124A PARTIAL DIFFERENTIAL EQUATIONS

LECTURE NOTES FOR MATH 124A PARTIAL DIFFERENTIAL EQUATIONS LECTURE NOTES FOR MATH 124A PARTIAL DIFFERENTIAL EQUATIONS S. SETO 1. Motivation for PDEs 1.1. What are PDEs? An algebraic equation is an equation which only involves algebraic operations, e.g. x 2 1 =.

More information

Strauss PDEs 2e: Section Exercise 2 Page 1 of 6. Solve the completely inhomogeneous diffusion problem on the half-line

Strauss PDEs 2e: Section Exercise 2 Page 1 of 6. Solve the completely inhomogeneous diffusion problem on the half-line Strauss PDEs 2e: Section 3.3 - Exercise 2 Page of 6 Exercise 2 Solve the completely inhomogeneous diffusion problem on the half-line v t kv xx = f(x, t) for < x

More information

Math 4263 Homework Set 1

Math 4263 Homework Set 1 Homework Set 1 1. Solve the following PDE/BVP 2. Solve the following PDE/BVP 2u t + 3u x = 0 u (x, 0) = sin (x) u x + e x u y = 0 u (0, y) = y 2 3. (a) Find the curves γ : t (x (t), y (t)) such that that

More information

Mathematical Methods - Lecture 9

Mathematical Methods - Lecture 9 Mathematical Methods - Lecture 9 Yuliya Tarabalka Inria Sophia-Antipolis Méditerranée, Titane team, http://www-sop.inria.fr/members/yuliya.tarabalka/ Tel.: +33 (0)4 92 38 77 09 email: yuliya.tarabalka@inria.fr

More information

Strauss PDEs 2e: Section Exercise 4 Page 1 of 6

Strauss PDEs 2e: Section Exercise 4 Page 1 of 6 Strauss PDEs 2e: Section 5.3 - Exercise 4 Page of 6 Exercise 4 Consider the problem u t = ku xx for < x < l, with the boundary conditions u(, t) = U, u x (l, t) =, and the initial condition u(x, ) =, where

More information

Partial differential equation for temperature u(x, t) in a heat conducting insulated rod along the x-axis is given by the Heat equation:

Partial differential equation for temperature u(x, t) in a heat conducting insulated rod along the x-axis is given by the Heat equation: Chapter 7 Heat Equation Partial differential equation for temperature u(x, t) in a heat conducting insulated rod along the x-axis is given by the Heat equation: u t = ku x x, x, t > (7.1) Here k is a constant

More information

UNIVERSITY OF MANITOBA

UNIVERSITY OF MANITOBA DATE: May 8, 2015 Question Points Score INSTRUCTIONS TO STUDENTS: This is a 6 hour examination. No extra time will be given. No texts, notes, or other aids are permitted. There are no calculators, cellphones

More information

MATH 425, FINAL EXAM SOLUTIONS

MATH 425, FINAL EXAM SOLUTIONS MATH 425, FINAL EXAM SOLUTIONS Each exercise is worth 50 points. Exercise. a The operator L is defined on smooth functions of (x, y by: Is the operator L linear? Prove your answer. L (u := arctan(xy u

More information

Math 311, Partial Differential Equations, Winter 2015, Midterm

Math 311, Partial Differential Equations, Winter 2015, Midterm Score: Name: Math 3, Partial Differential Equations, Winter 205, Midterm Instructions. Write all solutions in the space provided, and use the back pages if you have to. 2. The test is out of 60. There

More information

There are five problems. Solve four of the five problems. Each problem is worth 25 points. A sheet of convenient formulae is provided.

There are five problems. Solve four of the five problems. Each problem is worth 25 points. A sheet of convenient formulae is provided. Preliminary Examination (Solutions): Partial Differential Equations, 1 AM - 1 PM, Jan. 18, 16, oom Discovery Learning Center (DLC) Bechtel Collaboratory. Student ID: There are five problems. Solve four

More information

(The) Three Linear Partial Differential Equations

(The) Three Linear Partial Differential Equations (The) Three Linear Partial Differential Equations 1 Introduction A partial differential equation (PDE) is an equation of a function of 2 or more variables, involving 2 or more partial derivatives in different

More information

Math 342 Partial Differential Equations «Viktor Grigoryan

Math 342 Partial Differential Equations «Viktor Grigoryan Math 342 Partial Differential Equations «Viktor Grigoryan 15 Heat with a source So far we considered homogeneous wave and heat equations and the associated initial value problems on the whole line, as

More information

MIDTERM REVIEW FOR MATH The limit

MIDTERM REVIEW FOR MATH The limit MIDTERM REVIEW FOR MATH 500 SHUANGLIN SHAO. The limit Define lim n a n = A: For any ε > 0, there exists N N such that for any n N, a n A < ε. The key in this definition is to realize that the choice of

More information

Week 4 Lectures, Math 6451, Tanveer

Week 4 Lectures, Math 6451, Tanveer 1 Diffusion in n ecall that for scalar x, Week 4 Lectures, Math 6451, Tanveer S(x,t) = 1 exp [ x2 4πκt is a special solution to 1-D heat equation with properties S(x,t)dx = 1 for t >, and yet lim t +S(x,t)

More information

Analysis III (BAUG) Assignment 3 Prof. Dr. Alessandro Sisto Due 13th October 2017

Analysis III (BAUG) Assignment 3 Prof. Dr. Alessandro Sisto Due 13th October 2017 Analysis III (BAUG Assignment 3 Prof. Dr. Alessandro Sisto Due 13th October 2017 Question 1 et a 0,..., a n be constants. Consider the function. Show that a 0 = 1 0 φ(xdx. φ(x = a 0 + Since the integral

More information

Final Exam May 4, 2016

Final Exam May 4, 2016 1 Math 425 / AMCS 525 Dr. DeTurck Final Exam May 4, 2016 You may use your book and notes on this exam. Show your work in the exam book. Work only the problems that correspond to the section that you prepared.

More information

Homework for Math , Fall 2016

Homework for Math , Fall 2016 Homework for Math 5440 1, Fall 2016 A. Treibergs, Instructor November 22, 2016 Our text is by Walter A. Strauss, Introduction to Partial Differential Equations 2nd ed., Wiley, 2007. Please read the relevant

More information

A First Course of Partial Differential Equations in Physical Sciences and Engineering

A First Course of Partial Differential Equations in Physical Sciences and Engineering A First Course of Partial Differential Equations in Physical Sciences and Engineering Marcel B. Finan Arkansas Tech University c All Rights Reserved First Draft August 29 2 Preface Partial differential

More information

Some Aspects of Solutions of Partial Differential Equations

Some Aspects of Solutions of Partial Differential Equations Some Aspects of Solutions of Partial Differential Equations K. Sakthivel Department of Mathematics Indian Institute of Space Science & Technology(IIST) Trivandrum - 695 547, Kerala Sakthivel@iist.ac.in

More information

1 h 9 e $ s i n t h e o r y, a p p l i c a t i a n

1 h 9 e $ s i n t h e o r y, a p p l i c a t i a n T : 99 9 \ E \ : \ 4 7 8 \ \ \ \ - \ \ T \ \ \ : \ 99 9 T : 99-9 9 E : 4 7 8 / T V 9 \ E \ \ : 4 \ 7 8 / T \ V \ 9 T - w - - V w w - T w w \ T \ \ \ w \ w \ - \ w \ \ w \ \ \ T \ w \ w \ w \ w \ \ w \

More information

Introduction to Differential Equations

Introduction to Differential Equations Chapter 1 Introduction to Differential Equations 1.1 Basic Terminology Most of the phenomena studied in the sciences and engineering involve processes that change with time. For example, it is well known

More information

Applications of the Maximum Principle

Applications of the Maximum Principle Jim Lambers MAT 606 Spring Semester 2015-16 Lecture 26 Notes These notes correspond to Sections 7.4-7.6 in the text. Applications of the Maximum Principle The maximum principle for Laplace s equation is

More information

Math Partial Differential Equations 1

Math Partial Differential Equations 1 Math 9 - Partial Differential Equations Homework 5 and Answers. The one-dimensional shallow water equations are h t + (hv) x, v t + ( v + h) x, or equivalently for classical solutions, h t + (hv) x, (hv)

More information

Lecture 17: Section 4.2

Lecture 17: Section 4.2 Lecture 17: Section 4.2 Shuanglin Shao November 4, 2013 Subspaces We will discuss subspaces of vector spaces. Subspaces Definition. A subset W is a vector space V is called a subspace of V if W is itself

More information

Boundary conditions. Diffusion 2: Boundary conditions, long time behavior

Boundary conditions. Diffusion 2: Boundary conditions, long time behavior Boundary conditions In a domain Ω one has to add boundary conditions to the heat (or diffusion) equation: 1. u(x, t) = φ for x Ω. Temperature given at the boundary. Also density given at the boundary.

More information

Maxima and Minima. (a, b) of R if

Maxima and Minima. (a, b) of R if Maxima and Minima Definition Let R be any region on the xy-plane, a function f (x, y) attains its absolute or global, maximum value M on R at the point (a, b) of R if (i) f (x, y) M for all points (x,

More information

MATH 124A Solution Key HW 05

MATH 124A Solution Key HW 05 3. DIFFUSION ON THE HALF-LINE Solutions prepared by Jon Tjun Seng Lo Kim Lin, TA Math 24A MATH 24A Solution Key HW 5 3. DIFFUSION ON THE HALF-LINE. Solve u t ku x x ; u(x, ) e x ; u(, t) on the half-line

More information

Problem Set 1. This week. Please read all of Chapter 1 in the Strauss text.

Problem Set 1. This week. Please read all of Chapter 1 in the Strauss text. Math 425, Spring 2015 Jerry L. Kazdan Problem Set 1 Due: Thurs. Jan. 22 in class. [Late papers will be accepted until 1:00 PM Friday.] This is rust remover. It is essentially Homework Set 0 with a few

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

Partial Differential Equations

Partial Differential Equations M3M3 Partial Differential Equations Solutions to problem sheet 3/4 1* (i) Show that the second order linear differential operators L and M, defined in some domain Ω R n, and given by Mφ = Lφ = j=1 j=1

More information

Math 5440 Problem Set 5 Solutions

Math 5440 Problem Set 5 Solutions Math 5 Math 5 Problem Set 5 Solutions Aaron Fogelson Fall, 3 : (Logan,. # 3) Solve the outgoing signal problem and where s(t) is a known signal. u tt c u >, < t

More information

PH.D. PRELIMINARY EXAMINATION MATHEMATICS

PH.D. PRELIMINARY EXAMINATION MATHEMATICS UNIVERSITY OF CALIFORNIA, BERKELEY SPRING SEMESTER 207 Dept. of Civil and Environmental Engineering Structural Engineering, Mechanics and Materials NAME PH.D. PRELIMINARY EXAMINATION MATHEMATICS Problem

More information

e (x y)2 /4kt φ(y) dy, for t > 0. (4)

e (x y)2 /4kt φ(y) dy, for t > 0. (4) Math 24A October 26, 2 Viktor Grigoryan Heat equation: interpretation of the solution Last time we considered the IVP for the heat equation on the whole line { ut ku xx = ( < x

More information

MATH-UA 263 Partial Differential Equations Recitation Summary

MATH-UA 263 Partial Differential Equations Recitation Summary MATH-UA 263 Partial Differential Equations Recitation Summary Yuanxun (Bill) Bao Office Hour: Wednesday 2-4pm, WWH 1003 Email: yxb201@nyu.edu 1 February 2, 2018 Topics: verifying solution to a PDE, dispersion

More information

My signature below certifies that I have complied with the University of Pennsylvania s Code of Academic Integrity in completing this exam.

My signature below certifies that I have complied with the University of Pennsylvania s Code of Academic Integrity in completing this exam. My signature below certifies that I have complied with the University of Pennsylvania s Code of Academic Integrity in completing this exam. Signature Printed Name Math 241 Exam 1 Jerry Kazdan Feb. 17,

More information

Math 126 Final Exam Solutions

Math 126 Final Exam Solutions Math 126 Final Exam Solutions 1. (a) Give an example of a linear homogeneous PE, a linear inhomogeneous PE, and a nonlinear PE. [3 points] Solution. Poisson s equation u = f is linear homogeneous when

More information

6 Non-homogeneous Heat Problems

6 Non-homogeneous Heat Problems 6 Non-homogeneous Heat Problems Up to this point all the problems we have considered for the heat or wave equation we what we call homogeneous problems. This means that for an interval < x < l the problems

More information

MATH 220: MIDTERM OCTOBER 29, 2015

MATH 220: MIDTERM OCTOBER 29, 2015 MATH 22: MIDTERM OCTOBER 29, 25 This is a closed book, closed notes, no electronic devices exam. There are 5 problems. Solve Problems -3 and one of Problems 4 and 5. Write your solutions to problems and

More information

Wave Equation With Homogeneous Boundary Conditions

Wave Equation With Homogeneous Boundary Conditions Wave Equation With Homogeneous Boundary Conditions MATH 467 Partial Differential Equations J. Robert Buchanan Department of Mathematics Fall 018 Objectives In this lesson we will learn: how to solve the

More information

Suggested Solution to Assignment 7

Suggested Solution to Assignment 7 MATH 422 (25-6) partial diferential equations Suggested Solution to Assignment 7 Exercise 7.. Suppose there exists one non-constant harmonic function u in, which attains its maximum M at x. Then by the

More information

SAMPLE FINAL EXAM SOLUTIONS

SAMPLE FINAL EXAM SOLUTIONS LAST (family) NAME: FIRST (given) NAME: ID # : MATHEMATICS 3FF3 McMaster University Final Examination Day Class Duration of Examination: 3 hours Dr. J.-P. Gabardo THIS EXAMINATION PAPER INCLUDES 22 PAGES

More information

2.3 Calculus of variations

2.3 Calculus of variations 2.3 Calculus of variations 2.3.1 Euler-Lagrange equation The action functional S[x(t)] = L(x, ẋ, t) dt (2.3.1) which maps a curve x(t) to a number, can be expanded in a Taylor series { S[x(t) + δx(t)]

More information

Homework # , Spring Due 14 May Convergence of the empirical CDF, uniform samples

Homework # , Spring Due 14 May Convergence of the empirical CDF, uniform samples Homework #3 36-754, Spring 27 Due 14 May 27 1 Convergence of the empirical CDF, uniform samples In this problem and the next, X i are IID samples on the real line, with cumulative distribution function

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

Homework #3 Solutions

Homework #3 Solutions Homework #3 Solutions Math 82, Spring 204 Instructor: Dr. Doreen De eon Exercises 2.2: 2, 3 2. Write down the heat equation (homogeneous) which corresponds to the given data. (Throughout, heat is measured

More information

Math 220A - Fall 2002 Homework 5 Solutions

Math 220A - Fall 2002 Homework 5 Solutions Math 0A - Fall 00 Homework 5 Solutions. Consider the initial-value problem for the hyperbolic equation u tt + u xt 0u xx 0 < x 0 u t (x, 0) ψ(x). Use energy methods to show that the domain of dependence

More information

MATH115. Indeterminate Forms and Improper Integrals. Paolo Lorenzo Bautista. June 24, De La Salle University

MATH115. Indeterminate Forms and Improper Integrals. Paolo Lorenzo Bautista. June 24, De La Salle University MATH115 Indeterminate Forms and Improper Integrals Paolo Lorenzo Bautista De La Salle University June 24, 2014 PLBautista (DLSU) MATH115 June 24, 2014 1 / 25 Theorem (Mean-Value Theorem) Let f be a function

More information

M343 Homework 3 Enrique Areyan May 17, 2013

M343 Homework 3 Enrique Areyan May 17, 2013 M343 Homework 3 Enrique Areyan May 17, 013 Section.6 3. Consider the equation: (3x xy + )dx + (6y x + 3)dy = 0. Let M(x, y) = 3x xy + and N(x, y) = 6y x + 3. Since: y = x = N We can conclude that this

More information

AP Calculus Chapter 3 Testbank (Mr. Surowski)

AP Calculus Chapter 3 Testbank (Mr. Surowski) AP Calculus Chapter 3 Testbank (Mr. Surowski) Part I. Multiple-Choice Questions (5 points each; please circle the correct answer.). If f(x) = 0x 4 3 + x, then f (8) = (A) (B) 4 3 (C) 83 3 (D) 2 3 (E) 2

More information

Applied Mathematics Masters Examination Fall 2016, August 18, 1 4 pm.

Applied Mathematics Masters Examination Fall 2016, August 18, 1 4 pm. Applied Mathematics Masters Examination Fall 16, August 18, 1 4 pm. Each of the fifteen numbered questions is worth points. All questions will be graded, but your score for the examination will be the

More information

Chapter 2: First Order DE 2.6 Exact DE and Integrating Fa

Chapter 2: First Order DE 2.6 Exact DE and Integrating Fa Chapter 2: First Order DE 2.6 Exact DE and Integrating Factor First Order DE Recall the general form of the First Order DEs (FODE): dy dx = f(x, y) (1) (In this section x is the independent variable; not

More information

Functional Analysis HW 2

Functional Analysis HW 2 Brandon Behring Functional Analysis HW 2 Exercise 2.6 The space C[a, b] equipped with the L norm defined by f = b a f(x) dx is incomplete. If f n f with respect to the sup-norm then f n f with respect

More information

SOLUTION OF THE DIRICHLET PROBLEM WITH A VARIATIONAL METHOD. 1. Dirichlet integral

SOLUTION OF THE DIRICHLET PROBLEM WITH A VARIATIONAL METHOD. 1. Dirichlet integral SOLUTION OF THE DIRICHLET PROBLEM WITH A VARIATIONAL METHOD CRISTIAN E. GUTIÉRREZ FEBRUARY 3, 29. Dirichlet integral Let f C( ) with open and bounded. Let H = {u C ( ) : u = f on } and D(u) = Du(x) 2 dx.

More information

Math 220a - Fall 2002 Homework 6 Solutions

Math 220a - Fall 2002 Homework 6 Solutions Math a - Fall Homework 6 Solutions. Use the method of reflection to solve the initial-boundary value problem on the interval < x < l, u tt c u xx = < x < l u(x, = < x < l u t (x, = x < x < l u(, t = =

More information

1 Solution to Problem 2.1

1 Solution to Problem 2.1 Solution to Problem 2. I incorrectly worked this exercise instead of 2.2, so I decided to include the solution anyway. a) We have X Y /3, which is a - function. It maps the interval, ) where X lives) onto

More information

Theory of PDE Homework 2

Theory of PDE Homework 2 Theory of PDE Homework 2 Adrienne Sands April 18, 2017 In the following exercises we assume the coefficients of the various PDE are smooth and satisfy the uniform ellipticity condition. R n is always an

More information

12.7 Heat Equation: Modeling Very Long Bars.

12.7 Heat Equation: Modeling Very Long Bars. 568 CHAP. Partial Differential Equations (PDEs).7 Heat Equation: Modeling Very Long Bars. Solution by Fourier Integrals and Transforms Our discussion of the heat equation () u t c u x in the last section

More information

ASM Study Manual for Exam P, Second Edition By Dr. Krzysztof M. Ostaszewski, FSA, CFA, MAAA Errata

ASM Study Manual for Exam P, Second Edition By Dr. Krzysztof M. Ostaszewski, FSA, CFA, MAAA Errata ASM Study Manual for Exam P, Second Edition By Dr. Krzysztof M. Ostaszewski, FSA, CFA, MAAA (krzysio@krzysio.net) Errata Effective July 5, 3, only the latest edition of this manual will have its errata

More information

The Maximum and Minimum Principle

The Maximum and Minimum Principle MODULE 5: HEAT EQUATION 5 Lecture 2 The Maximum and Minimum Principle In this lecture, we shall prove the maximum and minimum properties of the heat equation. These properties can be used to prove uniqueness

More information

Lecture No 1 Introduction to Diffusion equations The heat equat

Lecture No 1 Introduction to Diffusion equations The heat equat Lecture No 1 Introduction to Diffusion equations The heat equation Columbia University IAS summer program June, 2009 Outline of the lectures We will discuss some basic models of diffusion equations and

More information

Lecture Notes in Mathematics. A First Course in Quasi-Linear Partial Differential Equations for Physical Sciences and Engineering Solution Manual

Lecture Notes in Mathematics. A First Course in Quasi-Linear Partial Differential Equations for Physical Sciences and Engineering Solution Manual Lecture Notes in Mathematics A First Course in Quasi-Linear Partial Differential Equations for Physical Sciences and Engineering Solution Manual Marcel B. Finan Arkansas Tech University c All Rights Reserved

More information

Solutions of Math 53 Midterm Exam I

Solutions of Math 53 Midterm Exam I Solutions of Math 53 Midterm Exam I Problem 1: (1) [8 points] Draw a direction field for the given differential equation y 0 = t + y. (2) [8 points] Based on the direction field, determine the behavior

More information

volq = U(f, P 2 ). This finally gives

volq = U(f, P 2 ). This finally gives MAT 257Y s to Practice Final (1) Let A R n be a rectangle and let f : A R be bounded. Let P 1, P 2 be two partitions of A. Prove that L(f, P 1 ) (f, P 2 ). The statement is obvious if P 1 = P 2. In general,

More information

Chain Rule. MATH 311, Calculus III. J. Robert Buchanan. Spring Department of Mathematics

Chain Rule. MATH 311, Calculus III. J. Robert Buchanan. Spring Department of Mathematics 3.33pt Chain Rule MATH 311, Calculus III J. Robert Buchanan Department of Mathematics Spring 2019 Single Variable Chain Rule Suppose y = g(x) and z = f (y) then dz dx = d (f (g(x))) dx = f (g(x))g (x)

More information

Partial Differential Equations Separation of Variables. 1 Partial Differential Equations and Operators

Partial Differential Equations Separation of Variables. 1 Partial Differential Equations and Operators PDE-SEP-HEAT-1 Partial Differential Equations Separation of Variables 1 Partial Differential Equations and Operators et C = C(R 2 ) be the collection of infinitely differentiable functions from the plane

More information

NONLOCAL DIFFUSION EQUATIONS

NONLOCAL DIFFUSION EQUATIONS NONLOCAL DIFFUSION EQUATIONS JULIO D. ROSSI (ALICANTE, SPAIN AND BUENOS AIRES, ARGENTINA) jrossi@dm.uba.ar http://mate.dm.uba.ar/ jrossi 2011 Non-local diffusion. The function J. Let J : R N R, nonnegative,

More information

MATH 18.01, FALL PROBLEM SET #5 SOLUTIONS (PART II)

MATH 18.01, FALL PROBLEM SET #5 SOLUTIONS (PART II) MATH 8, FALL 7 - PROBLEM SET #5 SOLUTIONS (PART II (Oct ; Antiderivatives; + + 3 7 points Recall that in pset 3A, you showed that (d/dx tanh x x Here, tanh (x denotes the inverse to the hyperbolic tangent

More information

Heat/Di usion Equation. 2 = 0 k constant w(x; 0) = '(x) initial condition. ( w2 2 ) t (kww x ) x + k(w x ) 2 dx. (w x ) 2 dx 0.

Heat/Di usion Equation.  2 = 0 k constant w(x; 0) = '(x) initial condition. ( w2 2 ) t (kww x ) x + k(w x ) 2 dx. (w x ) 2 dx 0. Hat/Di usion Equation @w @t k @ w @x k constant w(x; ) '(x) initial condition w(; t) w(l; t) boundary conditions Enrgy stimat: So w(w t kw xx ) ( w ) t (kww x ) x + k(w x ) or and thrfor E(t) R l Z l Z

More information

Differential equations

Differential equations Differential equations Math 27 Spring 2008 In-term exam February 5th. Solutions This exam contains fourteen problems numbered through 4. Problems 3 are multiple choice problems, which each count 6% of

More information

z x = f x (x, y, a, b), z y = f y (x, y, a, b). F(x, y, z, z x, z y ) = 0. This is a PDE for the unknown function of two independent variables.

z x = f x (x, y, a, b), z y = f y (x, y, a, b). F(x, y, z, z x, z y ) = 0. This is a PDE for the unknown function of two independent variables. Chapter 2 First order PDE 2.1 How and Why First order PDE appear? 2.1.1 Physical origins Conservation laws form one of the two fundamental parts of any mathematical model of Continuum Mechanics. These

More information

The first order quasi-linear PDEs

The first order quasi-linear PDEs Chapter 2 The first order quasi-linear PDEs The first order quasi-linear PDEs have the following general form: F (x, u, Du) = 0, (2.1) where x = (x 1, x 2,, x 3 ) R n, u = u(x), Du is the gradient of u.

More information

9 More on the 1D Heat Equation

9 More on the 1D Heat Equation 9 More on the D Heat Equation 9. Heat equation on the line with sources: Duhamel s principle Theorem: Consider the Cauchy problem = D 2 u + F (x, t), on x t x 2 u(x, ) = f(x) for x < () where f

More information

Partial Differential Equations Summary

Partial Differential Equations Summary Partial Differential Equations Summary 1. The heat equation Many physical processes are governed by partial differential equations. temperature of a rod. In this chapter, we will examine exactly that.

More information

MATH 131P: PRACTICE FINAL SOLUTIONS DECEMBER 12, 2012

MATH 131P: PRACTICE FINAL SOLUTIONS DECEMBER 12, 2012 MATH 3P: PRACTICE FINAL SOLUTIONS DECEMBER, This is a closed ook, closed notes, no calculators/computers exam. There are 6 prolems. Write your solutions to Prolems -3 in lue ook #, and your solutions to

More information

4.1 Analysis of functions I: Increase, decrease and concavity

4.1 Analysis of functions I: Increase, decrease and concavity 4.1 Analysis of functions I: Increase, decrease and concavity Definition Let f be defined on an interval and let x 1 and x 2 denote points in that interval. a) f is said to be increasing on the interval

More information

Math Homework 2

Math Homework 2 Math 73 Homework Due: September 8, 6 Suppose that f is holomorphic in a region Ω, ie an open connected set Prove that in any of the following cases (a) R(f) is constant; (b) I(f) is constant; (c) f is

More information

Math 260: Solving the heat equation

Math 260: Solving the heat equation Math 260: Solving the heat equation D. DeTurck University of Pennsylvania April 25, 2013 D. DeTurck Math 260 001 2013A: Solving the heat equation 1 / 1 1D heat equation with Dirichlet boundary conditions

More information

Final: Solutions Math 118A, Fall 2013

Final: Solutions Math 118A, Fall 2013 Final: Solutions Math 118A, Fall 2013 1. [20 pts] For each of the following PDEs for u(x, y), give their order and say if they are nonlinear or linear. If they are linear, say if they are homogeneous or

More information

ODE Homework Solutions of Linear Homogeneous Equations; the Wronskian

ODE Homework Solutions of Linear Homogeneous Equations; the Wronskian ODE Homework 3 3.. Solutions of Linear Homogeneous Equations; the Wronskian 1. Verify that the functions y 1 (t = e t and y (t = te t are solutions of the differential equation y y + y = 0 Do they constitute

More information

The concentration of a drug in blood. Exponential decay. Different realizations. Exponential decay with noise. dc(t) dt.

The concentration of a drug in blood. Exponential decay. Different realizations. Exponential decay with noise. dc(t) dt. The concentration of a drug in blood Exponential decay C12 concentration 2 4 6 8 1 C12 concentration 2 4 6 8 1 dc(t) dt = µc(t) C(t) = C()e µt 2 4 6 8 1 12 time in minutes 2 4 6 8 1 12 time in minutes

More information

( ) ( ). ( ) " d#. ( ) " cos (%) " d%

( ) ( ). ( )  d#. ( )  cos (%)  d% Math 22 Fall 2008 Solutions to Homework #6 Problems from Pages 404-407 (Section 76) 6 We will use the technique of Separation of Variables to solve the differential equation: dy d" = ey # sin 2 (") y #

More information

MA8109 Stochastic Processes in Systems Theory Autumn 2013

MA8109 Stochastic Processes in Systems Theory Autumn 2013 Norwegian University of Science and Technology Department of Mathematical Sciences MA819 Stochastic Processes in Systems Theory Autumn 213 1 MA819 Exam 23, problem 3b This is a linear equation of the form

More information

13 PDEs on spatially bounded domains: initial boundary value problems (IBVPs)

13 PDEs on spatially bounded domains: initial boundary value problems (IBVPs) 13 PDEs on spatially bounded domains: initial boundary value problems (IBVPs) A prototypical problem we will discuss in detail is the 1D diffusion equation u t = Du xx < x < l, t > finite-length rod u(x,

More information

Linear DifferentiaL Equation

Linear DifferentiaL Equation Linear DifferentiaL Equation Massoud Malek The set F of all complex-valued functions is known to be a vector space of infinite dimension. Solutions to any linear differential equations, form a subspace

More information

First Order Differential Equations

First Order Differential Equations Chapter 2 First Order Differential Equations Introduction Any first order differential equation can be written as F (x, y, y )=0 by moving all nonzero terms to the left hand side of the equation. Of course,

More information

C2 Differential Equations : Computational Modeling and Simulation Instructor: Linwei Wang

C2 Differential Equations : Computational Modeling and Simulation Instructor: Linwei Wang C2 Differential Equations 4040-849-03: Computational Modeling and Simulation Instructor: Linwei Wang Part II Variational Principle Calculus Revisited Partial Derivatives Function of one variable df dx

More information

Lecture 22: Section 4.7

Lecture 22: Section 4.7 Lecture 22: Section 47 Shuanglin Shao December 2, 213 Row Space, Column Space, and Null Space Definition For an m n, a 11 a 12 a 1n a 21 a 22 a 2n A = a m1 a m2 a mn, the vectors r 1 = [ a 11 a 12 a 1n

More information

Section 12.6: Non-homogeneous Problems

Section 12.6: Non-homogeneous Problems Section 12.6: Non-homogeneous Problems 1 Introduction Up to this point all the problems we have considered are we what we call homogeneous problems. This means that for an interval < x < l the problems

More information

2 (Bonus). Let A X consist of points (x, y) such that either x or y is a rational number. Is A measurable? What is its Lebesgue measure?

2 (Bonus). Let A X consist of points (x, y) such that either x or y is a rational number. Is A measurable? What is its Lebesgue measure? MA 645-4A (Real Analysis), Dr. Chernov Homework assignment 1 (Due 9/5). Prove that every countable set A is measurable and µ(a) = 0. 2 (Bonus). Let A consist of points (x, y) such that either x or y is

More information

Verona Course April Lecture 1. Review of probability

Verona Course April Lecture 1. Review of probability Verona Course April 215. Lecture 1. Review of probability Viorel Barbu Al.I. Cuza University of Iaşi and the Romanian Academy A probability space is a triple (Ω, F, P) where Ω is an abstract set, F is

More information

1 Review of di erential calculus

1 Review of di erential calculus Review of di erential calculus This chapter presents the main elements of di erential calculus needed in probability theory. Often, students taking a course on probability theory have problems with concepts

More information