This book teaches you all necessary (problem-independent) tools and techniques needed to implement and perform sophisticated scientific numerical simulations. Thus, it is suited for undergraduate and graduate students who want to become experts in computer simulations in Physics, Chemistry, Biology, Engineering, Computer Science and other fields. Content:
Programming in C, basics of C++, Python, make, shell scripts Software engineering, computational provenance, version management Debugging: gdb, memory checking, profiling Standard algorithms: iteration, recursion, divide-and-conquer, dynamic programing, backtracking Advanced data structures: lists, trees, heaps, graphs Libraries: standard C library, STL, GSL, self-written libraries Randomness: probability, discrete and continuous random variables, pseudo random numbers, inversion method, rejection method Data analysis: estimators, confidence intervals, histograms, resampling, plotting, Chi-squared test, Kolmogorov-Smirnov test, ROC analysis, principal component analysis, data clustering, maximum likelihood, fitting Presentation and publishing: gnuplot, xfig, Povray, LATEXContents: - Programming in C
- Scripts
- Software Engineering
- Debugging and Testing
- Object-oriented Software Development
- Algorithms and Data Structures
- Libraries
- Randomness and Statistics
- Information Retrieval, Publishing and Presentations
Readership: Advanced undergraduate and graduate students in biology, chemistry, computer science, engineering or physics. Also experienced researchers in these fields who start with computer simulations.
Key Features:
Provides all skills necessary to perform scientific simulation projects from the stage of program planning to the final stage of publishing the results in one concise treatment
Useful for simulation projects in all scientific fields, e.g. physics, chemistry, biology, mathematics, computer science, social science and the economy
