Question: I'm currently working on my MS in CS and have developed an interest in astrophysics. Luckily one of my professors is a astrophysicist and is currently doing research through computational physics and he agreed to help me get started.

I'm currently reading through Tao Pang's "An Introduction to Computational Physics" ([TOC](http://www.physics.unlv.edu/~pang/cp2_cont.html)), but the book's lack of detail on the mathematical methods used leaves me pretty lost at times.

My question is, are there any books that cover the topics in a more in-depth fashion or any books that I could use as a reference for the mathematical methods?

EDIT: The best way to describe my mathematical background is that I've studied calculus, linear algebra and differential equations, but remember very little.

Answer: 
From skimming the table of contents to the book you listed, I'd say that computational books of that type for physics (or in my case, engineering, since that is my background) tend to sacrifice depth and quality of explanation for breadth. The best book of this ilk I can think of is probably Strang's [Computational Science and Engineering](http://math.mit.edu/cse/), because he's a great writer of mathematics. Many "numerical methods" classes geared towards scientists and engineers tend to teach just enough math in whatever topics they feel the intended audience needs to get by, and cobble together a bunch of notes. (Or they punt, and teach out of [Burden and Faires](http://rads.stackoverflow.com/amzn/click/0495114766), or worse, rely on [Numerical Recipes](http://rads.stackoverflow.com/amzn/click/0521880688).)

To get better explanations and depth, you'll probably need to get access to more specialized textbooks, such as:

* dense linear algebra: probably [Numerical Linear Algebra](http://rads.stackoverflow.com/amzn/click/0898713617) by Trefethen and Bau, though I've also heard good things about [Applied Numerical Linear Algebra](http://rads.stackoverflow.com/amzn/click/0898713897) by Demmel, and [Matrix Computations](http://rads.stackoverflow.com/amzn/click/1421407949) by Golub and van Loan
* sparse linear algebra: for iterative methods, Saad's [Iterative Methods for Sparse Linear Systems](http://www-users.cs.umn.edu/~saad/IterMethBook_2ndEd.pdf), for direct methods, Davis' [Direct Methods for Sparse Linear Systems](http://rads.stackoverflow.com/amzn/click/0898716136), for multigrid, [A Multigrid Tutorial](http://rads.stackoverflow.com/amzn/click/0898714621), among others
* solving ODEs: [Computer Methods for Ordinary Differential and Differential-Algebraic Equations](http://rads.stackoverflow.com/amzn/click/0898714125) by Ascher and Petzold; also see the book by Lambert, the two volume series by Hairer and Wanner, and Butcher's book

A list could be created for optimization (Nocedal's textbook is a good start), finite volume methods (LeVeque's books), finite difference methods (again, LeVeque has another good book, or the book by Strikwerda), finite element methods (not an expert here), and so on. The tradeoff is depth (and quality of explanation) for time. I agree with Pedro that you'll probably be able to make the largest contributions right away in the algorithms and data structures used to implement physics. If you really want to dig into the math, though, I'd pick one of the specialty books that you think will be most useful to you and start reading it.
You might try: Bertil Gustafsson: *Fundamentals of Scientific Computing* to get an overview, and for a more encyclopaedic treatment of the mathematical methods Gander, Gander, Kwok: *Scientific Computing.*