Eleni Tsitinidi
Faculty Sponsor: Michelle Kuchera
This program adapts the Monte Carlo Random Walk algorithm to calculate the energy for the ground state of a finite square well. It also plots the unnormalized wave function and the Energy vs V for a range of V from 1 to 200. It changes the algorithm of random walk Monte Carlo as presented in “An Introduction to Computer Simulation Methods Applications to Physical System” to calculate different potentials (currently finite square well), runs in python and changes aspects of the algorithm such as when the “random walkers” are being added (adds multiple at a time). The energy estimate for the ground finite square potential well with the inputs of the user’s choosing is printed to the console. In addition a plot of the unnormalized wave function is returned along with a plot of how the energy ranges with different V values. The results are not very accurate numerically, they seem to be off by a magnitude of 10 but the graphs seem correct in shape. The method can and will be improved by using the Diffusion Monte Carlo improved algorithm.
This program adapts the Monte Carlo Random Walk algorithm to calculate the
energy for the ground state of a finite square well. It also plots the unnormalized wave function and the Energy vs V for a range of V from 1 to 200. It changes the algorithm of random walk Monte Carlo as presented in “An Introduction to Computer Simulation Methods Applications to Physical System” to calculate different potentials (currently finite square well), runs in python and changes aspects of the algorithm such as when the “random walkers” are being added (adds multiple at a time). The energy estimate for the ground finite square potential well with the inputs of the user’s choosing is printed to the console. In addition a plot of the unnormalized wave function is returned along with a plot of how the energy ranges with different V values. The results are not very accurate numerically, they seem to be off by a magnitude of 10 but the graphs seem correct in shape. The method can and will be improved by using the Diffusion Monte Carlo improved algorithm.
