Notes on Partial Differential Equations John K. Hunter
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Sobolev Spaces, Theory and Applications
Sobolev spaces, theory and applications Piotr Haj lasz1 Introduction These are the notes that I prepared for the participants of the Summer School in Mathematics in Jyv¨askyl¨a,August, 1998. I thank Pekka Koskela for his kind invitation. This is the second summer course that I delivere in Finland. Last August I delivered a similar course entitled Sobolev spaces and calculus of variations in Helsinki. The subject was similar, so it was not posible to avoid overlapping. However, the overlapping is little. I estimate it as 25%. While preparing the notes I used partially the notes that I prepared for the previous course. Moreover Lectures 9 and 10 are based on the text of my joint work with Pekka Koskela [33]. The notes probably will not cover all the material presented during the course and at the some time not all the material written here will be presented during the School. This is however, not so bad: if some of the results presented on lectures will go beyond the notes, then there will be some reasons to listen the course and at the same time if some of the results will be explained in more details in notes, then it might be worth to look at them. The notes were prepared in hurry and so there are many bugs and they are not complete. Some of the sections and theorems are unfinished. At the end of the notes I enclosed some references together with comments. This section was also prepared in hurry and so probably many of the authors who contributed to the subject were not mentioned. -
Lecture 11 Line Integrals of Vector-Valued Functions (Cont'd)
Lecture 11 Line integrals of vector-valued functions (cont’d) In the previous lecture, we considered the following physical situation: A force, F(x), which is not necessarily constant in space, is acting on a mass m, as the mass moves along a curve C from point P to point Q as shown in the diagram below. z Q P m F(x(t)) x(t) C y x The goal is to compute the total amount of work W done by the force. Clearly the constant force/straight line displacement formula, W = F · d , (1) where F is force and d is the displacement vector, does not apply here. But the fundamental idea, in the “Spirit of Calculus,” is to break up the motion into tiny pieces over which we can use Eq. (1 as an approximation over small pieces of the curve. We then “sum up,” i.e., integrate, over all contributions to obtain W . The total work is the line integral of the vector field F over the curve C: W = F · dx . (2) ZC Here, we summarize the main steps involved in the computation of this line integral: 1. Step 1: Parametrize the curve C We assume that the curve C can be parametrized, i.e., x(t)= g(t) = (x(t),y(t), z(t)), t ∈ [a, b], (3) so that g(a) is point P and g(b) is point Q. From this parametrization we can compute the velocity vector, ′ ′ ′ ′ v(t)= g (t) = (x (t),y (t), z (t)) . (4) 1 2. Step 2: Compute field vector F(g(t)) over curve C F(g(t)) = F(x(t),y(t), z(t)) (5) = (F1(x(t),y(t), z(t), F2(x(t),y(t), z(t), F3(x(t),y(t), z(t))) , t ∈ [a, b] . -
MULTIVARIABLE CALCULUS (MATH 212 ) COMMON TOPIC LIST (Approved by the Occidental College Math Department on August 28, 2009)
MULTIVARIABLE CALCULUS (MATH 212 ) COMMON TOPIC LIST (Approved by the Occidental College Math Department on August 28, 2009) Required Topics • Multivariable functions • 3-D space o Distance o Equations of Spheres o Graphing Planes Spheres Miscellaneous function graphs Sections and level curves (contour diagrams) Level surfaces • Planes o Equations of planes, in different forms o Equation of plane from three points o Linear approximation and tangent planes • Partial Derivatives o Compute using the definition of partial derivative o Compute using differentiation rules o Interpret o Approximate, given contour diagram, table of values, or sketch of sections o Signs from real-world description o Compute a gradient vector • Vectors o Arithmetic on vectors, pictorially and by components o Dot product compute using components v v v v v ⋅ w = v w cosθ v v v v u ⋅ v = 0⇔ u ⊥ v (for nonzero vectors) o Cross product Direction and length compute using components and determinant • Directional derivatives o estimate from contour diagram o compute using the lim definition t→0 v v o v compute using fu = ∇f ⋅ u ( u a unit vector) • Gradient v o v geometry (3 facts, and understand how they follow from fu = ∇f ⋅ u ) Gradient points in direction of fastest increase Length of gradient is the directional derivative in that direction Gradient is perpendicular to level set o given contour diagram, draw a gradient vector • Chain rules • Higher-order partials o compute o mixed partials are equal (under certain conditions) • Optimization o Locate and -
Engineering Analysis 2 : Multivariate Functions
Multivariate functions Partial Differentiation Higher Order Partial Derivatives Total Differentiation Line Integrals Surface Integrals Engineering Analysis 2 : Multivariate functions P. Rees, O. Kryvchenkova and P.D. Ledger, [email protected] College of Engineering, Swansea University, UK PDL (CoE) SS 2017 1/ 67 Multivariate functions Partial Differentiation Higher Order Partial Derivatives Total Differentiation Line Integrals Surface Integrals Outline 1 Multivariate functions 2 Partial Differentiation 3 Higher Order Partial Derivatives 4 Total Differentiation 5 Line Integrals 6 Surface Integrals PDL (CoE) SS 2017 2/ 67 Multivariate functions Partial Differentiation Higher Order Partial Derivatives Total Differentiation Line Integrals Surface Integrals Introduction Recall from EG189: analysis of a function of single variable: Differentiation d f (x) d x Expresses the rate of change of a function f with respect to x. Integration Z f (x) d x Reverses the operation of differentiation and can used to work out areas under curves, and as we have just seen to solve ODEs. We’ve seen in vectors we often need to deal with physical fields, that depend on more than one variable. We may be interested in rates of change to each coordinate (e.g. x; y; z), time t, but sometimes also other physical fields as well. PDL (CoE) SS 2017 3/ 67 Multivariate functions Partial Differentiation Higher Order Partial Derivatives Total Differentiation Line Integrals Surface Integrals Introduction (Continue ...) Example 1: the area A of a rectangular plate of width x and breadth y can be calculated A = xy The variables x and y are independent of each other. In this case, the dependent variable A is a function of the two independent variables x and y as A = f (x; y) or A ≡ A(x; y) PDL (CoE) SS 2017 4/ 67 Multivariate functions Partial Differentiation Higher Order Partial Derivatives Total Differentiation Line Integrals Surface Integrals Introduction (Continue ...) Example 2: the volume of a rectangular plate is given by V = xyz where the thickness of the plate is in z-direction. -
Lecture Notes for MATH 592A
Lecture Notes for MATH 592A Vladislav Panferov February 25, 2008 1. Introduction As a motivation for developing the theory let’s consider the following boundary-value problem, u′′ + u = f(x) x Ω=(0, 1) − ∈ u(0) = 0, u(1) = 0, where f is a given (smooth) function. We know that the solution is unique, sat- isfies the stability estimate following from the maximum principle, and it can be expressed explicitly through Green’s function. However, there is another way “to say something about the solution”, quite independent of what we’ve done before. Let’s multuply the differential equation by u and integrate by parts. We get x=1 1 1 u′ u + (u′ 2 + u2) dx = f u dx. − x=0 0 0 h i Z Z The boundary terms vanish because of the boundary conditions. We introduce the following notations 1 1 u 2 = u2 dx (thenorm), and (f,u)= f udx (the inner product). k k Z0 Z0 Then the integral identity above can be written in the short form as u′ 2 + u 2 =(f,u). k k k k Now we notice the following inequality (f,u) 6 f u . (Cauchy-Schwarz) | | k kk k This may be familiar from linear algebra. For a quick proof notice that f + λu 2 = f 2 +2λ(f,u)+ λ2 u 2 0 k k k k k k ≥ 1 for any λ. This expression is a quadratic function in λ which has a minimum for λ = (f,u)/ u 2. Using this value of λ and rearranging the terms we get − k k (f,u)2 6 f 2 u 2. -
Partial Derivatives
Partial Derivatives Partial Derivatives Just as derivatives can be used to explore the properties of functions of 1 vari- able, so also derivatives can be used to explore functions of 2 variables. In this section, we begin that exploration by introducing the concept of a partial derivative of a function of 2 variables. In particular, we de…ne the partial derivative of f (x; y) with respect to x to be f (x + h; y) f (x; y) fx (x; y) = lim h 0 h ! when the limit exists. That is, we compute the derivative of f (x; y) as if x is the variable and all other variables are held constant. To facilitate the computation of partial derivatives, we de…ne the operator @ = “The partial derivative with respect to x” @x Alternatively, other notations for the partial derivative of f with respect to x are @f @ f (x; y) = = f (x; y) = @ f (x; y) x @x @x x 2 2 EXAMPLE 1 Evaluate fx when f (x; y) = x y + y : Solution: To do so, we write @ @ @ f (x; y) = x2y + y2 = x2y + y2 x @x @x @x and then we evaluate the derivative as if y is a constant. In partic- ular, @ 2 @ 2 fx (x; y) = y x + y = y 2x + 0 @x @x That is, y factors to the front since it is considered constant with respect to x: Likewise, y2 is considered constant with respect to x; so that its derivative with respect to x is 0. Thus, fx (x; y) = 2xy: Likewise, the partial derivative of f (x; y) with respect to y is de…ned f (x; y + h) f (x; y) fy (x; y) = lim h 0 h ! 1 when the limit exists. -
Partial Differential Equations a Partial Difierential Equation Is a Relationship Among Partial Derivatives of a Function
In contrast, ordinary differential equations have only one independent variable. Often associate variables to coordinates (Cartesian, polar, etc.) of a physical system, e.g. u(x; y; z; t) or w(r; θ) The domain of the functions involved (usually denoted Ω) is an essential detail. Often other conditions placed on domain boundary, denoted @Ω. Partial differential equations A partial differential equation is a relationship among partial derivatives of a function (or functions) of more than one variable. Often associate variables to coordinates (Cartesian, polar, etc.) of a physical system, e.g. u(x; y; z; t) or w(r; θ) The domain of the functions involved (usually denoted Ω) is an essential detail. Often other conditions placed on domain boundary, denoted @Ω. Partial differential equations A partial differential equation is a relationship among partial derivatives of a function (or functions) of more than one variable. In contrast, ordinary differential equations have only one independent variable. The domain of the functions involved (usually denoted Ω) is an essential detail. Often other conditions placed on domain boundary, denoted @Ω. Partial differential equations A partial differential equation is a relationship among partial derivatives of a function (or functions) of more than one variable. In contrast, ordinary differential equations have only one independent variable. Often associate variables to coordinates (Cartesian, polar, etc.) of a physical system, e.g. u(x; y; z; t) or w(r; θ) Often other conditions placed on domain boundary, denoted @Ω. Partial differential equations A partial differential equation is a relationship among partial derivatives of a function (or functions) of more than one variable. -
Introduction to Sobolev Spaces
Introduction to Sobolev Spaces Lecture Notes MM692 2018-2 Joa Weber UNICAMP December 23, 2018 Contents 1 Introduction1 1.1 Notation and conventions......................2 2 Lp-spaces5 2.1 Borel and Lebesgue measure space on Rn .............5 2.2 Definition...............................8 2.3 Basic properties............................ 11 3 Convolution 13 3.1 Convolution of functions....................... 13 3.2 Convolution of equivalence classes................. 15 3.3 Local Mollification.......................... 16 3.3.1 Locally integrable functions................. 16 3.3.2 Continuous functions..................... 17 3.4 Applications.............................. 18 4 Sobolev spaces 19 4.1 Weak derivatives of locally integrable functions.......... 19 1 4.1.1 The mother of all Sobolev spaces Lloc ........... 19 4.1.2 Examples........................... 20 4.1.3 ACL characterization.................... 21 4.1.4 Weak and partial derivatives................ 22 4.1.5 Approximation characterization............... 23 4.1.6 Bounded weakly differentiable means Lipschitz...... 24 4.1.7 Leibniz or product rule................... 24 4.1.8 Chain rule and change of coordinates............ 25 4.1.9 Equivalence classes of locally integrable functions..... 27 4.2 Definition and basic properties................... 27 4.2.1 The Sobolev spaces W k;p .................. 27 4.2.2 Difference quotient characterization of W 1;p ........ 29 k;p 4.2.3 The compact support Sobolev spaces W0 ........ 30 k;p 4.2.4 The local Sobolev spaces Wloc ............... 30 4.2.5 How the spaces relate.................... 31 4.2.6 Basic properties { products and coordinate change.... 31 i ii CONTENTS 5 Approximation and extension 33 5.1 Approximation............................ 33 5.1.1 Local approximation { any domain............. 33 5.1.2 Global approximation on bounded domains....... -
Advanced Partial Differential Equations Prof. Dr. Thomas
Advanced Partial Differential Equations Prof. Dr. Thomas Sørensen summer term 2015 Marcel Schaub July 2, 2015 1 Contents 0 Recall PDE 1 & Motivation 3 0.1 Recall PDE 1 . .3 1 Weak derivatives and Sobolev spaces 7 1.1 Sobolev spaces . .8 1.2 Approximation by smooth functions . 11 1.3 Extension of Sobolev functions . 13 1.4 Traces . 15 1.5 Sobolev inequalities . 17 2 Linear 2nd order elliptic PDE 25 2.1 Linear 2nd order elliptic partial differential operators . 25 2.2 Weak solutions . 26 2.3 Existence via Lax-Milgram . 28 2.4 Inhomogeneous bounday value problems . 35 2.5 The space H−1(U) ................................ 36 2.6 Regularity of weak solutions . 39 A Tutorials 58 A.1 Tutorial 1: Review of Integration . 58 A.2 Tutorial 2 . 59 A.3 Tutorial 3: Norms . 61 A.4 Tutorial 4 . 62 A.5 Tutorial 6 (Sheet 7) . 65 A.6 Tutorial 7 . 65 A.7 Tutorial 9 . 67 A.8 Tutorium 11 . 67 B Solutions of the problem sheets 70 B.1 Solution to Sheet 1 . 70 B.2 Solution to Sheet 2 . 71 B.3 Solution to Problem Sheet 3 . 73 B.4 Solution to Problem Sheet 4 . 76 B.5 Solution to Exercise Sheet 5 . 77 B.6 Solution to Exercise Sheet 7 . 81 B.7 Solution to problem sheet 8 . 84 B.8 Solution to Exercise Sheet 9 . 87 2 0 Recall PDE 1 & Motivation 0.1 Recall PDE 1 We mainly studied linear 2nd order equations – specifically, elliptic, parabolic and hyper- bolic equations. Concretely: • The Laplace equation ∆u = 0 (elliptic) • The Poisson equation −∆u = f (elliptic) • The Heat equation ut − ∆u = 0, ut − ∆u = f (parabolic) • The Wave equation utt − ∆u = 0, utt − ∆u = f (hyperbolic) We studied (“main motivation; goal”) well-posedness (à la Hadamard) 1. -
Advanced Topics in Markov Chains
Advanced Topics in Markov chains J.M. Swart April 25, 2018 Abstract This is a short advanced course in Markov chains, i.e., Markov processes with discrete space and time. The first chapter recalls, with- out proof, some of the basic topics such as the (strong) Markov prop- erty, transience, recurrence, periodicity, and invariant laws, as well as some necessary background material on martingales. The main aim of the lecture is to show how topics such as harmonic functions, coupling, Perron-Frobenius theory, Doob transformations and intertwining are all related and can be used to study the properties of concrete chains, both qualitatively and quantitatively. In particular, the theory is applied to the study of first exit problems and branching processes. 2 Notation N natural numbers f0; 1;:::g N+ positive natural numbers f1; 2;:::g NN [ f1g Z integers ZZ [ {−∞; 1g Q rational numbers R real numbers R extended real numbers [−∞; 1] C complex numbers B(E) Borel-σ-algebra on a topological space E 1A indicator function of the set A A ⊂ BA is a subset of B, which may be equal to B Ac complement of A AnB set difference A closure of A int(A) interior of A (Ω; F; P) underlying probability space ! typical element of Ω E expectation with respect to P σ(:::) σ-field generated by sets or random variables kfk1 supremumnorm kfk1 := supx jf(x)j µ ν µ is absolutely continuous w.r.t. ν fk gk lim fk=gk = 0 fk ∼ gk lim fk=gk = 1 o(n) any function such that o(n)=n ! 0 O(n) any function such that supn o(n)=n ≤ 1 δx delta measure in x µ ⊗ ν product measure of µ and ν ) weak convergence of probability laws Contents 0 Preliminaries 5 0.1 Stochastic processes . -
Delta Functions and Distributions
When functions have no value(s): Delta functions and distributions Steven G. Johnson, MIT course 18.303 notes Created October 2010, updated March 8, 2017. Abstract x = 0. That is, one would like the function δ(x) = 0 for all x 6= 0, but with R δ(x)dx = 1 for any in- These notes give a brief introduction to the mo- tegration region that includes x = 0; this concept tivations, concepts, and properties of distributions, is called a “Dirac delta function” or simply a “delta which generalize the notion of functions f(x) to al- function.” δ(x) is usually the simplest right-hand- low derivatives of discontinuities, “delta” functions, side for which to solve differential equations, yielding and other nice things. This generalization is in- a Green’s function. It is also the simplest way to creasingly important the more you work with linear consider physical effects that are concentrated within PDEs, as we do in 18.303. For example, Green’s func- very small volumes or times, for which you don’t ac- tions are extremely cumbersome if one does not al- tually want to worry about the microscopic details low delta functions. Moreover, solving PDEs with in this volume—for example, think of the concepts of functions that are not classically differentiable is of a “point charge,” a “point mass,” a force plucking a great practical importance (e.g. a plucked string with string at “one point,” a “kick” that “suddenly” imparts a triangle shape is not twice differentiable, making some momentum to an object, and so on. -
1 Subharmonic Functions 2 Harmonic Functions
1 Subharmonic Functions LTCC course on Potential Theory, Spring 2011 Boris Khoruzhenko1, QMUL This course follows closely a textbook on Potential Theory in the Complex Plane by Thomas Ransford, published by CUP in 1995. Five two-hour lectures will cover the following 1. Harmonic functions: basic properties, maximum principle, mean-value property, positive harmonic functions, Harnack's Theorem 2. Subharmonic functions: maximum principle, local integrability 3. Potentials, polar sets, equilibrium measures 4. Dirichlet problem, harmonic measure, Green's function 5. Capacity, transfinite diameter, Bernstein-Walsh Theorem 2 Harmonic Functions (Lecture notes for Day 1, 21 Feb 2011, revised 24 Feb 2011) 2.1 Harmonic and holomorphic functions Let D be domain (connected open set) in C. We shall consider complex- valued and real valued functions of z = x + iy 2 D. We shall write h(z) or often simply h to denote both a function of complex z and a function in two variables x and y. Notation: hx will be used to denote the partial derivative of h with respect to variable x, similarly hxx, hyy, hxy and hyx are the second order partial derivatives of f. Here come our main definition of the day. Recall that C2(D) denotes the space of functions on D with continuous second order derivatives. Definition A function h : D ! R is called harmonic on D if h 2 C2(D) and hxx + hyy = 0 on D. 1E-mail: [email protected], URL: http://maths.qmul.ac.ukn ∼boris 1 Examples 2 2 2 (a) h(z) = jzj = x + y is not harmonic anywhere on C as hxx + hyy=2.