What are some of the more practical applications, if there are any, of computational physics?
Actually I can think of quite a lot - and this is mostly possible due to the increases in computing power and better algorithms over the last 2 decades.
Here is a good overview from Caltech - http://library.caltech.edu/sherman/collections.htm
Science and engineering memebers of the Materials Research Society publish quite a lot about computational physics as applied to materials - e.g. http://www.mrs.org/meetings/spring2000/program/updsym/ProgramBookO.html
I am sure ZapperZ and Gokul could add quite a bit on this subject.
Computational physics has applications to pretty much all research going on in physics. Typically you can use more complex models to compare with experiment by using computer simulations. I'm doing stuff like this right now involving some probes on a tokamak. The situation in the vicinity of the probe is just too complicated to come up with any sort of decent analytical solution, so it turns out that it is better to use what is called a particle-in-cell (PIC) simulation to model the situation in order to interpret the data collected by the probe.
There's huge amount of computational work done in condensed matter physics. Electron structures, dielectric constants and so on..
My standard answer in something like this is to to go the American Physical Society website (www.aps.org) and look at the specific division/units under the APS. Practically all branches of physics are represented here. If you go to each of the division/units page, you'll find a wealth of links for that particular field of study.
For example, you'll find this for the Divisionn of Computational Physics
A reasonable number of computational physicists switch fields and work in Wall-Street (or elsewhere in the finance sector) modeling extremely complex, non-linear behavior.
The point is that, as a computational physicist, you learn to model very tricky stuff. For instance, there's a grad student in my dept. (Physics) using Ising Models and related statistical mechanics methods to simulate and predict geographical variations in socio-economic behavior.
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