I've often written about how all the various branches of science and becoming more and more computationally oriented. Computational biology, physics, math, geometry, chemistry and all the rest. Computational science is everywhere. Modelling and data analysis are everywhere. Bioinformatics is the hottest thing going these days.
An interesting post on the Wired Science blog Correlations where Michael Tobis writes about computational science as the Third Branch of Science:
Some people these days are saying that computing has become so important to science that it constitutes a third branch. Even though computationally intensive science is what occupies my time, I am not sure that this is the right way to think about it philosophically. To some extent computing brings the theoretical and experimental branches closer together.
There are some sciences where the experimental method is extremely limited. Earth sciences are among them, but the quintessential example is astrophysics. We simply can't afford to be monkeying around with stars to test our theories about how they work!
In fields where experimentation and direct observation are limited, computational science is especially important. Computational science is about simulation. Once we know the equations which describe a physical system, we can program a computer with that description and watch the simulated system just as we would watch a real system.
This is a tradeoff; in fact our understanding is imperfect, so the simulated system won't behave exactly like the real system. (In fact, more often than not it doesn't behave even remotely like the real system, but you don't hewar about these back-to-the-drawing-board efforts. Perhaps it might be better if failures were better documented to prevent others from going down certain blind alleys repeatedly, but in general failed efforts are just abandoned.)
If the imperfect simulation looks somewhat like the observable parts of the real system, though, it has tremendous benefits. The model is perfectly observable. We can investigate phenomena in ways that could never be affordable to measure in the real world.
Worth reading the whole article.