ankitpangeni / simulation_modeling

Simulation and Modeling topics for lab.

Simulation_Modeling

The GitHub repository includes a collection of C and C++ codes that cover various topics related to simulation and modeling. The codes are designed to be used as tools for exploring and analyzing different aspects of simulation,with a particular focus on Monte Carlo simulation, pseudorandom number generation, continuous system simulation, chi-square testing, and poker testing for random numbers.

Monte Carlo simulation of pie provides an engaging way to understand the concept of randomness and probability. The code generates a large number of random points within a square and then determines how many of those points fall within a circle inscribed in the square. This is a classic example of how the Monte Carlo method can be used to approximate the value of pi.

Another important topic is implementation of pseudorandom number generators. These generators are essential in simulation and modeling, as they allow for the creation of random events within a controlled environment. The code in this repository demonstrates how to generate high-quality random numbers using various methods, such as the Mixed congruential method and Linear congruental method.

Continuous system simulation is another important topic covered by the repository. The code provides an implementation of continuous-time simulation, which is a technique used to model systems that evolve continuously. It describes a chemical reaction.

The repository also includes code for chi-square testing, which is a statistical method used to determine if an observed distribution is significantly different from an expected distribution. This code can be used to analyze data from a variety of sources, such as experimental results or survey data. Here we generate random data.

Finally, the poker test for random numbers is another important feature of the repository. This test is used to determine if a sequence of random numbers is truly random, or if there are any patterns or biases in the sequence. The code in this repository can be used to test any pseudorandom number generator and can help ensure that simulations are accurate and reliable.

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