Muqri, M. R. (2020, June), Carlo Simulations  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34263

\bibitem{asee_peer_34263}
  Muqri, M. R. (2020, June), \emph{Carlo Simulations}
  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line .
  10.18260/1-2--34263

Mohammad Rafiq Muqri.  "Carlo Simulations".  2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June.  ASEE Conferences, 2020.  https://peer.asee.org/34263 Internet.  12 Jan, 2025

\bibitem{asee_peer_34263}
  Mohammad Rafiq Muqri.
  "Carlo Simulations".
  \emph{2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June}.
  ASEE Conferences, 2020.
  https://peer.asee.org/34263 Internet.  12 Jan, 2025

@INPROCEEDINGS{asee_peer_34263
author = "Mohammad Rafiq Muqri"
title = "Carlo Simulations"
booktitle = "2020 ASEE Virtual Annual Conference Content Access"
year = "2020"
month = "June"
address = "Virtual On line "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/34263}
number = {10.18260/1-2--34263}
}

TY  - CPAPER
AB  - LEVERAGING THE POWER OF C#, PYTHON AND MATLAB FOR STOCHASTIC OR MONTE CARLO SIMULATIONS

Monte Carlo simulation is one specific multivariate modeling technique that allows researchers to run multiple trials and define all potential outcomes of an event or investment. In various scientific and industrial fields, stochastic simulations are taking on a new importance. Advances in high performance computing have enhanced the practitioners’ aim to simulate more and more complex systems, use random parameters as well as random noises to model the parametric uncertainties of these systems. The error analysis of these computations is a highly complex mathematical undertaking. 
Monte Carlo Methods are applied in two ways: simulation and sampling. Simulation refers to methods of providing mathematical imitation of real random phenomenon. Sampling refers to methods of deducing properties of a large set of elements by studying only a small, random subset. The paper will explore selected programming tools, theoretical analysis of stochastic numerical methods, and practical issues at a high level, so as to provide optimal results on the accuracy of Monte Carlo simulations of stochastic processes.
The techniques discussed in this teaching module were meant to help students to apply them to simulate particle transport, components reliability, risk analysis, molecular modeling and many other fields such as electron beam lithography or electron microscopy.
The world of stochastic simulations is very rich and varied. Due to numerous simulation examples using C#,  Python and Matlab  programming snippets depicting  graphical visualizations, it is expected that this teaching module will not only benefit students and instructors but also physicists, biologists, economists and other professionals working with stochastic simulations,
As a result, there will be a discussion concerning the comparison of Matlab, C# and Python programming tools as well as students’ feedback. The result of this new approach is expected to strengthen the capacity and quality of our degree programs and enhance overall student learning and satisfaction.


AU  - Mohammad Rafiq Muqri
CY  - Virtual On line 
DA  - 2020/06/22
PB  - ASEE Conferences
TI  - Carlo Simulations
UR  - https://peer.asee.org/34263
DO  - 10.18260/1-2--34263
ER  -