In recent years, computer simulation has become an
increasingly important tool in nearly every area of
scientific endeavor. The computational scientist, once
struggling to prove that his contributions had merit at all,
is becoming ever more relied upon to treat problems that
simply can't be approached experimentally due to time, space,
or other logistical limitations. In this article, we'll
take a look at the basic components of computer simulation in
the sciences and lightly touch on some of the specific
applications being made in the various disciplines.
The goals of scientists setting out to do computer
simulation can vary widely. Maybe an experimentalist has done
a volume of work trying to ascertain a reliable measure of
some physical property of a material. Having performed an
experiment many times and also having read the literature on
the subject, he is unable to pin down a consistent result. In
this case, he may turn to first-principles and use simulation
as a way to guide him in the right direction in terms of
tuning his experimental setup.
In another scenario, a sociology researcher may want to
predict the average blood cholesterol of a population based on
environmental changes that are likely to occur in the next few
millenia. In this case, obviously, the scientist can't hold
his subjects to experimental scrutiny, and so he must in some
way model what he hopes to study. Under these conditions,
simulation forms the entire "experiment" used to reach a
conclusion.
While the two researchers discussed above use simulation to
varying degrees and with different expectations, some
similarities in their methods persist. First, and most
obvious, is the fact that both scientists must employ a model
for the aspect of reality they wish to examine. The models
employed by computational scientists rely on physical and
mathematical laws to construct a virtual representation of the
system of interest. This reliance on physics and math leads
to the second similarity, namely the formulation of some sort
of approximation. For you see, except in certain very simple
cases, these laws become so complicated that they can't be
applied in a straightforward manner to reach the desired
result. Aside from the special problems where so-called
analytical solutions are possible, then, scientists must
devise ways around the technical difficulties which arise.
These "tricks" have varying degrees of validity and will
doubtless be discussed in more depth in a future piece.
Having outlined the basics of scientific computer
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