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While I agree that the undergraduate ordinary differential equations class has major issues, numerical methods are not quite as easy and straightforward as you
by btrettel 6y ago
While I agree that the undergraduate ordinary differential equations class has major issues, numerical methods are not quite as easy and straightforward as you indicate. I've worked in computational fluid dynamics (CFD) before and often there is not a cookie-cutter algorithm you can apply to a problem like a RK scheme for an ODE.
The statement "you can obtain solution to any DE with any degree of precision" is practically not true for many systems. Despite massive increases in computational power, I don't know anyone who (e.g.) believes that turbulence will be solved any time in the next few decades solely by throwing more computing power at the problem. Through a combination of experimentation, analysis, and computation, however, we have made a lot of progress.
One major advantage of analytical methods is that they more easily allow you to see larger scale trends in the problem. If a computation is expensive, calculating sensitivities can be difficult, and in my experience sensitivities are found in CFD less often than they should be. Computation in some sense is more like experimentation: it gives you a local value. You need to do multiple runs to look at trends.
I think most people also underestimate how far analytical methods can get. In my PhD, I produced an accurate approximate solution to a nonlinear series of ODEs relevant to the problem I was studying. This gave a lot of insight into the problem that researchers using numerical methods for decades apparently couldn't figure out.
By the way, I approximated the unsolvable nonlinear system of ODEs there as a different nonlinear system of ODEs, albeit an exactly solvable system. Most people linearize, but that would have lead to a far less accurate solution in this case. Many nonlinear differential equations can be solved exactly; you can see a catalog of these at this website and in the associated books: http://eqworld.ipmnet.ru/ http://eqworld.ipmnet.ru/
It's really quite similar to integral tables. You look up the form of the equation you have and they tell you the solution (often with a proof or citation for the proof) or at the very least give you a head start if they don't have the solution.
I'm working on another theory project right now and I intend to use the same "approximate an unsolveable nonlinear system with a solveable nonlinear system" trick here.
- na85 6y agoVery interesting. Is there a similarity criterion or criteria you apply when searching for systems that you can use as approximates?
- btrettel 6y agoGreat question. Right now I'm working mostly on intuition, though I certainly could formalize the process. One thing I'm planning to do for my current project is to make the solution match the linearized solution for short times. While I haven't proved this, it seems obvious to me that particular terms which are being approximated would need to have the same linearization as the original term. (Incidentally, the approximation I used in my PhD did not have this property, but in that case I wasn't interested in short times.) Otherwise, the process is going to be trial and error. I'll look through the catalog of solveable nonlinear ODEs, try to fit my square peg into a round hole that might be plausible and see if it works. If I get multiple possible approximate solutions I'll compare them in terms of accuracy (compare with the numerical computation) and ease of writing the solution out. Some solutions will be more irritating than others. Some could be longer than others. I also would prefer an explicit algebraic equation as the solution to an implicit equation. The case in my PhD started out as an implicit algebraic equation which I later was able to write explicitly through the use of a special function (which fortunately is implemented in Python, Matlab, etc.). More practically speaking, the easiest nonlinear ODE to integrate is autonomous: https://en.wikipedia.org/wiki/Autonomous_system_(mathematics) https://en.wikipedia.org/wiki/Autonomous_system_(mathematics... Most likely my new project will be converted to a single nonlinear autonomous ODE. The question then is which autonomous approximation is best. Also: The approximation I used in my PhD was only valid for a particular range of values of one parameter. It took some physical intuition to recognize the approximation, which I later realized other people had used long before me in different contexts.