Two kind of views for thesis in Scientific Computing
In my earlier post, I've summarized what is Scientific Computing, area where I spent many hours these days, that I may become a distracted husband, dad and boyfriend. 
Here, I'd like to highlight the "content" of my reference. Materials that I've been digging. Here you go:
-
Systems of Linear Equations
-
Linear Least Squares
-
Eigenvalues and Singular Values
-
Nonlinear Equations
-
Optimization
-
Interpolation
-
Numerical Integration and Differentiation
-
Ordinary Differential Equations
-
Partial Differential Equations
-
Fast Fourier Transform
-
Random Numbers and Stochastic Simulation
I remedy these materials with two different views:
-
First view, mastering these known techniques/numerical algorithms so that I can solve problems in various domains that need numerical solutions (the domain can be Physics, Engineering, Biology, Economics, etc). Here, I learn the "tools" so that I can use them to solve other problems.
-
Second view, looking for intrinsic issues within the techniques/numerical algorithm so that I can improve the techniques/numerical algorithms. Here, a domain problem is not necessarily needed. Here, I learn the "tools" so that I can make better "tools".
I haven't decided my thesis would be from which view of the two above. I would need to consult my mentor. The second view might be harder, I think. But then, if I have to focus on the first view, I still have no idea to solve problems in what domain. Physics/Engineering or Computer Science as I have background on it? Biology? Economics? or.... Mathematics itself!
That sounds interesting, using computing to solve problems within Math! I got the idea after I got these two books:
From the two books I was introduced to a re-emerge discipline now called Experimental Mathematics. Hmm, something to discuss with my mentor.
Btw, is there any of you familiar with Experimental Mathematics or someone doing it?
Phew! I really need to get title for my thesis.