Class Meeting # 1: Introduction to PDEs

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1 MATH COURSE NOTES - CLASS MEETING # Introduction to PDEs, Fall 2011 Professor: Jared Speck Class Meeting # 1: Introduction to PDEs 1. What is a PDE? We will be studying functions u = u(x 1, x 2,, x n ) and their partial derivatives. Here x 1, x 2,, x n are standard Cartesian coordinates on R n. We sometimes use the alternate notation u(x, y), u(x, y, z), etc. We also write e.g. u(r, θ, φ) for spherical coordinates on R 3, etc. We sometimes also have a time coordinate t, in which case t, x 1,, x n denotes standard Cartesian coordinates on R 1+n. We also use the alternate notation x 0 = t. We use lots of different notation for partial derivatives: (1.0.1a) u = u x i = i u, 1 i n, x i 2 u (1.0.1b) = u = u x i x j = i j u, 1 i, j n. x i x j x i x j 2 If i = j, then we sometimes abbreviate i ju = i u. If u is a function of (x, y), then we also write u x = u, etc. x Definition A PDE in a single unknown u is an equation involving u and its partial derivatives. All such equations can be written as 1 2 n (1.0.2) F (u, ux 1,, u x n, u x 1 x 1,, u x i 1 x i N, x, x,, x ) = 0, i 1,, i N {1, 2,, n} for some function F. Here N is called the order of the PDE. N is the maximum number of derivatives appearing in the equation. Example u = u(t, x) (1.0.3) 2 u + (1 + cos u) 3 t xu = 0 is a third-order nonlinear PDE. Example u = u(t, x) (1.0.4) 2 t u xu + u = t is a second-order linear PDE. We say that (1.0.4) is a constant coefficient linear PDE because u and its derivatives appear linearly (i.e. first power only) and are multiplied only by constants. 1

2 2 MATH COURSE NOTES - CLASS MEETING # 1 Example u = u(t, x) (1.0.5) t u + 2(1 + x 2 ) 3 xu + u = t is a third-order linear PDE. We say that (1.0.5) is a variable coefficient linear PDE because u and its derivatives appear linearly (i.e. first power only) and are multiplied only by functions of the coordinates (t, x). Example u = u(t, x), v = v(t, x) (1.0.6a) t u + 2x x v = sin(x 2 ), (1.0.6b) t v x 2 x u 0 is a system of PDEs in the unknowns u, v. 2. The Goals of PDE (and of this course) Suppose that we are interested in some physical system. A very fundamental question is: Which PDEs are good models for the system? A major goal of modeling is to answer this question. There is no general recipe for answering it! In practice, good models are often the end result of confrontations between experimental data and theory. In this course, we will discuss some important physical systems and the PDEs that are commonly used to model them. Now let s assume that we have a PDE that we believe is a good model for our system of interest. Then most of the time, the primary goals of PDE are to answer questions such as the following: (1) Does the PDE have any solutions? (Some PDEs have NO SOLUTIONS whatsoever!!) (2) What kind of data do we need to specify in order to solve the PDE? (3) Are the solutions corresponding to the given data unique? (4) What are the basic qualitative properties of the solution? (5) Does the solution contain singularities? If so, what is their nature? (6) What happens if we slightly vary the data? Does the solution then also vary only slightly? (7) What kinds of quantitative estimates can be derived for the solutions? (8) How can we ine the size (i.e., the norm ) of a solution in way that is useful for the problem at hand? 3. Physical Examples It is difficult to exaggerate how prevalent PDEs are. We will discuss some important physically motivated examples throughout this course. Here is a first look. t 2 u + xu 2 = 0 wave equation, second-order, linear, homogeneous t u + xu 2 = 0 heat equation, second-order, linear, homogeneous xu 2 + yu 2 + zu 2 = 0 Laplace s equation, second-order, linear, homogeneous xu 2 + yu 2 + zu 2 = f(x, y, z) Poisson s equation with source function f, second-order, linear, inhomogeneous (unless f = 0) ı t u + xu 2 = 0 Schrödinger s equation, second-order, linear, homogeneous u t + u x = 0, transport equation, first-order, linear, homogeneous u t + uu x = 0, Burger s equation, first-order, nonlinear, homogeneous

3 MATH COURSE NOTES - CLASS MEETING # 1 3 E = ( E 1 (x, y, z), E 2 (x, y, z), E 3 (x, y, z) ), B = ( ) B 1 (x, y, z), B 2 (x, y, z), B 3 (x, y, z) are vectors in R 3 (3.0.7a) t E B = 0, E = 0, (3.0.7b) t B + E = 0, B = 0 Maxwell s equations in a vacuum (i.e., matter-free spacetime), first-order, linear, homogeneous. 4. Linear PDEs Before we dive into a specific model, let s discuss a distinguished class of PDEs that are relatively easy to study. The PDEs of interest are called linear PDEs. Most of this course will concern linear PDEs. Definition A linear differential operator L is a differential operator such that (4.0.8) L(au + bv) = alu + blv for all constants a, b R and all functions u, v. Remark The notation was introduced out of convenience and laziness. The inition is closely connected to the superposition principle. Example L = 2 + (t 2 x 2 ) x 2 is a linear operator: Lu = t 2 u + (t 2 x 2 ) xu 2 t Example u = u(x, y), Lu = xu 2 + u 2 yu 2 does NOT ine a linear operator: L(u + v) = x(u 2 + v) + (u + v) 2 y(u 2 + v) = x u + u y u + x v + v y v = Lu + Lv Definition A PDE is linear if it can be written as (4.0.9) Lu = f(x 1,, x n ) for some linear operator L and some function f of the coordinates. Definition If f = 0, then we say that the PDE is homogeneous. Otherwise, we say that it is inhomogeneous. Example u = u(t, x) (4.0.10) t u (1 + cos t) xu 2 = tx is a linear PDE. Here is an incredibly useful property of linear PDEs. Proposition (Superposition principle). If u 1,, u M are solutions to the linear PDE (4.0.11) Lu = 0, and c 1,, cm R, then i=1 c iu i is also a solution. M

4 4 MATH COURSE NOTES - CLASS MEETING # 1 Proof. (4.0.12) L c i u i = M i=1 i=1 M =0 {}}{ ci Lu i = 0. Remark This shows that the set of all solutions to Lu = 0 is a vector space when L is linear. As we will see in the next proposition, inhomogeneous and homogeneous linear PDEs are closely related. Proposition (Relationship between the inhomogeneous and homogeneous linear PDE solutions). Let S h be the set of all solutions to the homogeneous linear PDE (4.0.13) Lu = 0, and let u I be a fixed solution to the inhomogeneous linear PDE (4.0.14) Lu = f(x 1,, x n ). Then the set S I of all solutions to (4.0.14) is the translation of S H by u I : S I = {u I + u H u H S H }. Proof. Assume that Lu I = f, and let w be any other solution to (4.0.14), i.e., Lw = f. Then L(w u I ) = f f = 0, so that w u I S H. Thus, w = u I + (w u } {{ I } ), and so w S I belongs to S H by inition. On the other hand, if w S I, then w = u I + u H for some u H S H. Therefore, Lw = L(u I + u H ) = Lu I + Lu H = f + 0 = f. Thus, w is a solution to (4.0.14). 5. How to solve PDEs There is no general recipe that works for all PDEs! We will develop some tools that will enable us to analyze some important classes of PDEs. Usually, we don t have explicit formulas for the solutions to the PDEs we are interested in! Instead, we are forced to understand and estimate the solutions without having explicit formulas. The two things that you typically need to study a PDE: You need to know the PDE. You need some data. 6. Some simple PDEs that we can easily solve 6.1. Constant coefficient transport equations. Consider the first-order linear transport equation (6.1.1) a x u(x, y) + b y u(x, y) = 0, where a, b R. Let s try to solve this PDE by reasoning geometrically. Geometrically, this equation says that u v = 0, where u = ( x u, y u) and v is the vector (a, b) R 2. Thus, the derivative of

5 MATH COURSE NOTES - CLASS MEETING # 1 5 u in the direction (a, b) is 0, which implies that u is constant along lines pointing in the direction of (a, b). The slope of such a line is b. Therefore, every such line can be described as the set of a solutions to bx ay = c, where c R. Since u is constant along these lines, we know that u is a function that depends only on the line c. Therefore u(x, y) = f(c) = f(bx ay) for some function f. In order to provide more details about u, we would need to prescribe some data. For example, if it is known that u(x, 0) = x 2, then x 2 = f(bx). Thus, f(c) = b 2 c 2, and u(x, y) = (x b 1 ay) 2. In the future, we will discuss the kinds of data that can be specified in more detail. As we will see, the type of data will depend on the type of PDE Solving a variable coefficient transport equations. With only a bit of additional effort, the procedure from Section 6.1 can be extended to cover the case where the coefficients are prespecified functions of x, y. Let s consider the following example: (6.2.1) y x u + x y u = 0. Let P denote a point P = (x, y), and let V denote the vector V = (y, x). Using vector calculus notation, (6.2.1) can be written as u(p ) V = 0, i.e., the derivative of u at P in the direction of V is 0. Thus, equation (6.2.1) implies that u is constant along the curve C passing through P that points in the same direction as V. This vector can be viewed as a line segment with slope x. y Therefore, if the curve C is parameterized by x (x, y(x)) (where we are viewing y as a function of x along C) then C has slope dy, and y is therefore a solution to the following ODE: dx dy x (6.2.2) =. dx y We can use the following steps to integrate (6.2.2), which you might have learned in an ODE class: dy 1 d (6.2.3) (6.2.2) = y = x = (y 2 ) = x dx 2 dx y 2 x 2 (6.2.4) = = + c, c = constant. 2 2 Thus, the curve C is a hyperbola of the form {y 2 x 2 = c}. These curves are called characteristics. We conclude that u is constant along the hyperbolas {y 2 x 2 = c}, which implies that u(x, y) = f(x 2 y 2 ) for some function f(c). We can carry out the same procedure for a PDE of the form (6.2.5) a(x, y) x u + b(x, y) y u = 0, as long as we can figure out how to integrate the ODE dy b(x, y) (6.2.6) =. dx a(x, y) 7. Some basic analytical notions and tools We now discuss a few ideas from analysis that will appear repeatedly throughout the course.

6 6 MATH COURSE NOTES - CLASS MEETING # Norms. In PDE, there are many different ways to measure the size of a function f. These measures are called norms. Here is a simple, but useful norm that will appear throughout this course. Definition (C k norms). Let f be a function ined on a domain Ω R. Then for any integer k 0, we ine the C k norm of f on Ω by (7.1.1) f C k (Ω) = sup f (a) (x), k x Ω a=0 where f (a) (x) is the a th order derivative of f(x). We often omit the symbol Ω when Ω = R. Example (7.1.2) sin(x) C 7 (R) = 8. The same notation is used in the case that Ω R n, but in this case, we now sum over all partial derivatives of order k. For example, if Ω R 2, then f C 2 (Ω) = sup (x,y) Ω f(x, y) + sup (x,y) Ω x f(x, y) +sup (x,y) Ω y f(x, y) +sup (x,y) Ω xf(x, 2 y) +sup (x,y) Ω x y f(x, y) +sup (x,y) Ω yf(x, 2 y). If f is a function of more than one variable, then we sometimes want to extract different information about f in one variable compared to another. For example, if f = f(t, x), then we use notation such as 1 2 a a (7.1.3) f C 1,2 = sup t f(t, x) + sup x f(t, x). (t,x) 2 a=0 R a=1 (t,x) R 2 Above, the 1 in C 1,2 refers to the t coordinate, while the 2 refers to the x coordinate. The next inition provides a very important example of another class of norms that are prevalent in PDE theory. Definition (L p norms). Let 1 p < be a number, and let f be a function ined on a domain Ω R n. We ine the L p norm of f by ( (7.1.4) f L p (Ω) = We often write just L p instead of L p (R n ). L p (Ω) has all the properties of a norm: Ω ) 1/p f(x) p d n x. Non-negativity: f L p (Ω) 0, f L p (Ω) = 0 f(x) = 0 almost everywhere 1 Scaling: λf L p (Ω) = λ f L p (Ω) Triangle inequality: f + g L p (Ω) f L p (Ω) + g L p (Ω) Similarly, C k (Ω) also has all the properties of a norm. All of these properties are very easy to show except for the last one in the case of L p (Ω). You will study the very important case p = 2 in detail in your homework. 1 Almost everywhere is a term that would be precisely ined in a course on measure theory.

7 MATH COURSE NOTES - CLASS MEETING # The divergence theorem. A lot of PDE results are derived using integration by parts (sometimes very fancy versions of it), which provides us with integral identities. This will become more apparent as the course progresses. Let s recall a very important version of integration by parts from vector calculus: the divergence theorem. We first need to recall the notion of a vectorfield on R n. Definition (Vectorfield). Recall that a vectorfield F on Ω R n is an R n valued (i.e. vector-valued) function ined on Ω. That is, (7.2.1) F : Ω R n, F(x 1,, x n ) = ( ) F 1 (x 1,, x n ),, F n (x 1,, x n ), where each of the F i are scalar-valued functions on R n. We also need to recall the inition of the divergence operator, which is a differential operator that acts on vectorfields. Definition (Divergence). Recall that F, the divergence of F, is the scalar-valued function on R n ined by (7.2.2) F = i F i. n i=1 We are now ready to recall the divergence theorem. Theorem 7.1 (Divergence Theorem). Let Ω R 3 be a domain 2 with a boundary that we denote by Ω. Then the following formula holds: (7.2.3) F(x, y, z) dxdydz = Ω Ω F(σ) N(σ) ˆ dσ. ˆ Above, N(σ) is the unit outward normal vector to Ω, and dσ is the surface measure induced on Ω. Recall that if Ω R 3 can locally be described as the graph of a function φ(x, y) (e.g., Ω = {(x, y, z) z = φ(x, y)}), then (7.2.4) dσ = 1 + φ(x, y) 2 dxdy, where φ = ( x φ, y φ) is the gradient of φ, and φ = ( x φ) 2 + ( y φ) 2 is the Euclidean length of φ. Remark The divergence theorem holds in all dimensions, not just 3. In dimension 1, the divergence theorem is d (7.2.5) F (x) dx = F (b) F (a), [a,b] dx which is just the Fundamental Theorem of Calculus. 2 Throughout this course, a domain is ined to be an open, connected subset of R n.

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