Why is there a difference between the two differential equations:
$\overset{.}{x}(t)=f(x(t))$ and $\overset{.}{x}(t)=f(t,x)$ ?
Why is there a difference between the two differential equations:
$\overset{.}{x}(t)=f(x(t))$ and $\overset{.}{x}(t)=f(t,x)$ ?
On
So you have a parameter $t$, a function $x$ which depends on $t$. In your first equation you have a function $f$ which does something to $x$, which you can then express as a function of $t$ by substituting that in (although since you don't know $x(t)$, this is a moot point). In the second equation, you have a function of two variables, $x$ and $t$, and you can do anything you want to $x$ or $t$ (within reason).
This is quite important if you want to actually solve the equation. The first is only as hard to solve as $1/f(x)$ is to integrate. The second can be much harder to solve in general.
On
There is much difference. The first only depends on $x$ (which is parameterized by $t$), so $t$ does not appear explicitly in the equation. The second one can be time-dependent (if $t$ is interpreted as time).
In physics, the difference would be something like this:
The first equation describes a phenomenon, where the velocity of an object only depends on its position. The second equation can have arbitrary driving forces present, that change in time and space.
If there was a second derivative on the left, the difference would mean that in the first case, the energy is conserved and in the second it probably isn't (autonomous versus driven system).
On
As already pointed out in the comments, there is a difference between both expression. Here are two relevant links you can check out. But first let's state the following:
You can always transform a system of $\overset{.}{x}(t)=f(t,x(t))$ into a system of $\overset{.}{x}(t)=f(x(t))$ by going $1$ dimension higher, it is a quite basic procedure.
Your second one $\overset{.}{x}(t)=f(t,x(t))\equiv\overset{.}{x}(t)=f(t,x)$ describes a general ordinary differential equation of order $1$.
Your first one $\overset{.}{x}(t)=f(x(t))$ is a special case of the second one, a so called autonomous system or time-invariant system.
When I see $x(t)$, I think of it as a path (or particle) moving depending on $t$ (time).
$f(x(t))$ is a function on the position of the particle only. Therefore, even if the particle returns to the same point more than once, the value of $f(x(t))$ is the same every time. An example of such a function is $f(x(t))=2x(t)$.
On the other hand, $f(t,x(t))$ means that $t$ is an input to the function as well. Therefore, if $x(t_1)=x(t_2)$, then $f(t_1,x(t_1))$ might not be the same as $f(t_2,x(t_2))$ because the function can depend on $t$. For example, you might have $f(t,x(t))=2tx(t)$.