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Welcome back! We continue chapter 16 with a discussion of vector fields. Essentially, a vector field is a function with the same input dimensions as output dimensions. In `R^2` and `R^3`, we visualize this as a bunch of vectors permeating all of space. If you've taken physics, you've probably encountered vector fields - the electric and magnetic fields are in fact vector fields! A way to think about vector fields is as the velocity of a given infinitesimal region of a fluid: it's at a position, and the velocity vector is pointing in some direction with some magnitude. This is actually a very good way to represent a flowing fluid, for our purposes.
An example of a vector field we've used extensively thus far is the gradient function of a surface! At every point along the surface, it outputs a vector representing the slope at that point.
One of the most useful operations we can perform on a vector field is a line integral (remember 16.1?). The integral of a vector field over a line is a fairly abstract concept, but it has real-world uses: imagine we have a function for the force at any given point, and we want to know the total force over a line. Vector fields! The form for a vector field integral is simply the dot product of the vector field and the tangent vector to the point on our line: given a line `r(t)` in a vector field `f(x, y) = [u, v]`, we have `int f(x, y) * dr`. This is one of those very elegant things that has interesting consequences.
Let's do an example. Straight from the textbook, we have `f(x, y, z) = \[z, xy, -y^2]` and `r(t) = \[t^2, t, sqrt(t)]`, where `a <= t <= b`. Getting `f(x, y, z)` in terms of `t` is simple enough: just substitute the components of `r`: `f(t) = \[sqrt(t), t^3, -t^2]`. `dr` is going to be `frac {dr} {dt} * dt`, so let's find `frac {dr} {dt}`: `frac d {dt} \[t^2, t, sqrt(t)] = \[2t, 1, frac 1 {2sqrt(t)}]`. So our integral is `int_a^b \[sqrt(t), t^3, -t^2] * \[2t, 1, frac 1 {2sqrt(t)}] dt`. Evaluating the dot product gives us `int_a^b 2t^{3/2} + t^3 - frac {t^{3/2}} {2} dt`. I'll leave further simplification as an exercise to the reader.
This section is cut rather short, as the introduced idea is simple and it's too late at night to try making it more complex. I might expand it with more examples and applications later. Section 16.3 will go into quite a bit more detail, as we'll be dealing with the problem of conservative vs nonconservative paths. For now, da svidanya and bonne nuit!
P.S: If you notice broken or missing math, it's probably due to my template engine freaking out about the square brackets. Will fix in a bit!
[/] [=title "Multivariable Exam 3 Review: Thomas 16.2"] [=author "Tyler Clarke"] [=date "2025-4-3"] [=subject "Calculus"] [#post.html]