Nothing specific: for physics to work, you need Calculus. I took both @ the same time in high school, and I learned the end function for most of my Calculus in Physics because the functions filled the need for physics to work. Over time, I came to the conclusion: that for things to work, they must be explainable. That's where the math comes in, and why is is called "the language of science". Sometimes, for the math to be understandable, you need to be able to prove math with yet more math. So, almost any good math that is checkable will have a use. From Wiki: "Advantages These models are popular for the following reasons. 1. Polynomial models have a simple form. 2. Polynomial models have well known and understood properties. 3. Polynomial models have moderate flexibility of shapes. 4. Polynomial models are a closed family. Changes of location and scale in the raw data result in a polynomial model being mapped to a polynomial model. That is, polynomial models are not dependent on the underlying metric. 5. Polynomial models are computationally easy to use. [edit] Disadvantages However, polynomial models also have the following limitations. 1. Polynomial models have poor interpolatory properties. High-degree polynomials are notorious for oscillations between exact-fit values. 2. Polynomial models have poor extrapolatory properties. Polynomials may provide good fits within the range of data, but they will frequently deteriorate rapidly outside the range of the data. 3. Polynomial models have poor asymptotic properties. By their nature, polynomials have a finite response for finite x values and have an infinite response if and only if the x value is infinite. Thus polynomials may not model asymptotic phenomena very well. 4. While no procedure is immune to the bias-variance tradeoff, polynomial models exhibit a particularly poor tradeoff between shape and degree. In order to model data with a complicated structure, the degree of the model must be high, indicating that the associated number of parameters to be estimated will also be high. This can result in highly unstable models. When modeling via polynomial functions is inadequate due to any of the limitations above, the use of rational functions for modeling may give a better fit." Basically, if I have a string of data that I want to represent with an equation, when the data is not linear, I go with different polynomial functions, but the polynomial will only be useful inside the data that is already given. You cannot realistically guess what the next set of data will be. In Physics, when explaining how a football can be thrown up into the air, and how it comes back down, an upside-down parabola is used, with where shape crosses the x axis (on the right side of the y axis) being where the football finally hits the ground. Where it crosses the y axis is the height from which it is thrown (i.e. someone 6'5" isn't throwing the ball from the ground but where his hands naturally rest at the release of the ball. When figuring out how much Vitamin A will kill you verses how much is needed to make an improvement in the host body, a J curve is used (half a parabola, still upside down). Go here for more: http://en.wikipedia.org/wiki/List_of_polynomial_topics