Lecture Notes in Differential Equations - Bruce E. Shapiro
Lecture Notes in Differential Equations - Bruce E. Shapiro Lecture Notes in Differential Equations - Bruce E. Shapiro
374 LESSON 34. CRITICAL POINTS variable with differential equation z = t, with differential equation z ′ = 1, and adding the third equation to the system: x ′ = cos y + x 2 (34.5) y ′ = sin x + e az (34.6) z ′ = 1 (34.7) We will focus on the general two dimensional autonomous system x ′ = f(x, y) x(t 0 ) = x 0 y ′ = g(x, y) y(t 0 ) = y 0 (34.8) where f and g are differentiable functions of x and y in some open set containing the point (x 0 , y 0 ). From the uniqueness theorem, we know that there is precisely one solution {x(t), y(t)} to (34.8). We can plot this solution as a curve that goes through the point (x 0 , y 0 ) and extends in either direction for some distance. We call this curve the trajectory of the solution. The xy-plane itself we will call the phase-plane. We must take care to distinguish trajectories from solutions: the trajectory is a curve in the xy plane, while the solution is a set of points that are marked by time. Different solutions follow the same trajectory, but at different times. The difference can be illustrated by the following analogy. Imagine that you drive from home to school every day on certain road, say Nordhoff Street. Every day you drive down Nordhoff from the 405 freeway to Reseda Boulevard. On Mondays and Wednesdays, you have morning classes and have to drive this path at 8:20 AM. On Tuesdays and Thursdays you have evening classes and you drive the same path, moving in the same direction, but at 3:30 PM. Then Nordhoff Street is your trajectory. You follow two different solutions: one which puts you at the 405 at 8:20 AM and another solution that puts at the 405 at 3:30 PM. Both solutions follow the same trajectory, but at different times. Returning to differential equations, an autonomous system with initial conditions x(t 0 ) = a, y(t 0 ) = b will have the same trajectory as an autonomous system with initial conditions x(t 1 ) = a, y(t 1 ) = b, but it will be a different solution because it will be at different points along the trajectory at different times. In any set where f(x, y) ≠ 0 we can form the differential equation dy g(x, y) = dx f(x, y) (34.9)
375 Since both f and g are differentiable, their quotient is differentiable (away from regions wheref = 0) and hence Lipshitz; consequently the initial value problem dy g(x, y) = dx f(x, y) , y(x 0) = y 0 (34.10) has a unique solution, which corresponds to the trajectory of equation (34.8) through the point (x 0 , y 0 ). Since the solution is unique, we conclude that there is only one trajectory through any point, except possibly wheref(x, y) = 0. A plot showing the one-parameter family of solutions to (34.9), annotated with arrows to indicate the direction of motion in time, is called a phase portrait of the system. Example 34.2. Let {x 1 , y 1 } and {x 2 , y 2 } be the solutions of and respectively. x ′ = −y, x(0) = 1 (34.11) y ′ = x, y(0) = 0 (34.12) x ′ = −y, x(π/4) = 1 (34.13) y ′ = x, y(π/4) = 0 (34.14) The solutions are different, but both solutions follow the same trajectory. To see this we solve the system by forming the second order differential equation representing this system: differentiate the second equation to obtain y ′′ = x ′ ; then substitute the first equation to obtain y ′′ = −y (34.15) The characteristic equation is r 2 +1 = 0 with roots of ±i; hence the solutions are linear combinations of sines and cosines. As we have seen, the general solution to this system is therefore y = A cos t + B sin t (34.16) x = −A sin t + B cos t (34.17) where the second equation is obtained by differentiating the first (because x = y ′ ).
- Page 331 and 332: 323 where (A − λI)w 2 = w 1 , i.
- Page 333 and 334: 325 so that λ = 3, −5. The eigen
- Page 335 and 336: 327 Non-constant Coefficients We ca
- Page 337 and 338: 329 we find that ∫ M(t)g(t)dt = (
- Page 339 and 340: Lesson 32 The Laplace Transform Bas
- Page 341 and 342: 333 Figure 32.1: A piecewise contin
- Page 343 and 344: 335 Example 32.4. From integral A.1
- Page 345 and 346: 337 apply this result iteratively.
- Page 347 and 348: L [ t x−1] [ ] 1 d = L x dt tx =
- Page 349 and 350: 341 Equating numerators and expandi
- Page 351 and 352: 343 Derivatives of the Laplace Tran
- Page 353 and 354: 345 can be written as as illustrate
- Page 355 and 356: 347 Translations in the Laplace Var
- Page 357 and 358: 349 Summary of Translation Formulas
- Page 359 and 360: 351 The inverse transform is [ ] f(
- Page 361 and 362: 353 Example 32.18. Find the Laplace
- Page 363 and 364: 355 Similarly, we can express a uni
- Page 365 and 366: 357 Figure 32.7: Solution of exampl
- Page 367 and 368: Lesson 33 Numerical Methods Euler
- Page 369 and 370: 361 Figure 33.1: Illustration of Eu
- Page 371 and 372: 363 y 4 = y 3 + hf(t 3 , y 3 ) (33.
- Page 373 and 374: 365 Figure 33.3: Illustration of th
- Page 375 and 376: 367 result with a smaller step size
- Page 377 and 378: 369 Expanding the final term in a T
- Page 379 and 380: 371 k 2 = y 0 + h 2 f(t 0, k 1 ) (3
- Page 381: Lesson 34 Critical Points of Autono
- Page 385 and 386: 377 Using the cos π/4 = √ 2/2 an
- Page 387 and 388: 379 values, of the matrix. We find
- Page 389 and 390: 381 Distinct Real Nonzero Eigenvalu
- Page 391 and 392: 383 eigendirection {λ 1 , v 1 }dom
- Page 393 and 394: 385 Figure 34.5: Phase portraits ty
- Page 395 and 396: 387 Complex Conjugate Pair with non
- Page 397 and 398: 389 The angular change is described
- Page 399 and 400: 391 Figure 34.8: Topological instab
- Page 401 and 402: 393 Figure 34.10: phase portraits f
- Page 403 and 404: Appendix A Table of Integrals Basic
- Page 405 and 406: 397 ∫ x √ x − adx = 2 3 a(x
- Page 407 and 408: 399 ∫ x √ ax2 + bx + c dx = 1 a
- Page 409 and 410: 401 ∫ ∫ ∫ ∫ e ax2 dx = −
- Page 411 and 412: 403 ∫ tan 3 axdx = 1 a ln cos ax
- Page 413 and 414: 405 Products of Trigonometric Funct
- Page 415 and 416: Appendix B Table of Laplace Transfo
- Page 417 and 418: 409 e at cosh kt t sin kt t cos kt
- Page 419 and 420: Appendix C Summary of Methods First
- Page 421 and 422: 413 The resulting equation is linea
- Page 423 and 424: 415 for y once z is known. Method o
- Page 425 and 426: Bibliography [1] Bear, H.S. Differe
- Page 427 and 428: BIBLIOGRAPHY 419
- Page 429 and 430: BIBLIOGRAPHY 421
374 LESSON 34. CRITICAL POINTS<br />
variable with differential equation z = t, with differential equation z ′ = 1,<br />
and add<strong>in</strong>g the third equation to the system:<br />
x ′ = cos y + x 2 (34.5)<br />
y ′ = s<strong>in</strong> x + e az (34.6)<br />
z ′ = 1 (34.7)<br />
We will focus on the general two dimensional autonomous system<br />
x ′ = f(x, y) x(t 0 ) = x 0<br />
y ′ = g(x, y) y(t 0 ) = y 0<br />
(34.8)<br />
where f and g are differentiable functions of x and y <strong>in</strong> some open set<br />
conta<strong>in</strong><strong>in</strong>g the po<strong>in</strong>t (x 0 , y 0 ). From the uniqueness theorem, we know that<br />
there is precisely one solution {x(t), y(t)} to (34.8). We can plot this solution<br />
as a curve that goes through the po<strong>in</strong>t (x 0 , y 0 ) and extends <strong>in</strong> either<br />
direction for some distance. We call this curve the trajectory of the solution.<br />
The xy-plane itself we will call the phase-plane.<br />
We must take care to dist<strong>in</strong>guish trajectories from solutions: the trajectory<br />
is a curve <strong>in</strong> the xy plane, while the solution is a set of po<strong>in</strong>ts that<br />
are marked by time. Different solutions follow the same trajectory, but at<br />
different times. The difference can be illustrated by the follow<strong>in</strong>g analogy.<br />
Imag<strong>in</strong>e that you drive from home to school every day on certa<strong>in</strong> road, say<br />
Nordhoff Street. Every day you drive down Nordhoff from the 405 freeway<br />
to Reseda Boulevard. On Mondays and Wednesdays, you have morn<strong>in</strong>g<br />
classes and have to drive this path at 8:20 AM. On Tuesdays and Thursdays<br />
you have even<strong>in</strong>g classes and you drive the same path, mov<strong>in</strong>g <strong>in</strong> the<br />
same direction, but at 3:30 PM. Then Nordhoff Street is your trajectory.<br />
You follow two different solutions: one which puts you at the 405 at 8:20<br />
AM and another solution that puts at the 405 at 3:30 PM. Both solutions<br />
follow the same trajectory, but at different times.<br />
Return<strong>in</strong>g to differential equations, an autonomous system with <strong>in</strong>itial conditions<br />
x(t 0 ) = a, y(t 0 ) = b will have the same trajectory as an autonomous<br />
system with <strong>in</strong>itial conditions x(t 1 ) = a, y(t 1 ) = b, but it will be a different<br />
solution because it will be at different po<strong>in</strong>ts along the trajectory at<br />
different times.<br />
In any set where f(x, y) ≠ 0 we can form the differential equation<br />
dy g(x, y)<br />
=<br />
dx f(x, y)<br />
(34.9)