dkotschessaa said:
Fantastic idea for an article. Would love to see if anybody has similar ideas for mathematics research, (although I would have liked to see it about 4 years ago when I was trying to do undergrad research). I have some ideas but little time to elaborate them now.
When I worked at the Air Force Academy, the Dept of Mathematical Sciences took a broad view on what fulfilled their "senior research" course requirements. I mentored a couple of their senior projects - one was working toward a three dimensional (two inputs, one output) cumulative probability function for standard weight in fish that would allow computing a standard weight equation with fewer data points than existing methods; another was an applied method to better understand rocket motor performance. I'm fairly certain that the committee would have approved just about any applied project a senior wanted to do that was heavy in math, regardless of the application.
A lot of very applied math also pops up in the Math category at ISEF-affiliated state and regional science fairs. Projects that might otherwise compete in Animal Sciences, Environmental Science, Biomedical and Health Sciences, etc. often compete in Mathematics if the data analysis is sufficiently intricate. Last year they added a new "Computational Biology and Bioinformatics" category to provide a place for projects that often competed in Mathematics.
When we work with students in brainstorming student research projects, we usually touch on a variety of projects that one can group under the general category of "
Numerical Experiments." The idea of these is that the student formulates a hypothesis on which computational or analysis approach might be better for some situation, with a clear definition of what better means in the context: faster, more accurate, simpler, etc. Then one conducts a numerical experiment to test the hypothesis. The student may create data using a spreadsheet with known good inputs plus added Gaussian noise, and then conduct the numerical experiments with different analysis techniques trying to recover the known good inputs.
There are also lots of questions in chaos and dynamics of complex systems that can be answered with
numerical experiments that simply numerically integrate the system of ODEs describing the system. Computers are fast enough these days that the numerical integration can be done with Runge-Kutta methods, so the numerical experiments are a matter of exploring phase space to determine how much of it is chaotic and varying system parameters to observe the transition from order to chaos as the knobs are turned. As a physicist, I tend to find Hamiltonian systems the most interesting, but I'm sure real math professors can suggest a variety of interesting non-Hamiltonian systems.