GridCal, a cross-platform power systems software written in Python with user interface, used in academia and industry.
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Updated
Jun 7, 2024 - MATLAB
GridCal, a cross-platform power systems software written in Python with user interface, used in academia and industry.
This is a collection of scripts and functions that serve as exercises for understanding and writing various numerical methods from scratch.
This project focuses on the simulation of the temperature field of the LITT device, calculating the distribution of photons in the cancer resistance and the resulting temperature change by Monte Carlo and finite difference methods, respectively.
This project show cases how to use Object Oriented Programming in MATLAB to do dynamic population models in a scalable and flexible framework
McCloud provides a generic service implementation of Monte Carlo method, based on Microsoft Windows Azure, to solve a wide range of scientific and engineering problems.
RandBar is a code to simulate the nonlinear stochastic dynamics of a bar structural system with attached discrete elements.
ORCHARD is a code for simulation of the nonlinear dynamics of an orchard sprayer tower.
A selection of scripts for constructing different ice crystal habits, and calculating their capacitances. Licensed under the GPL
OpenCossan is an open and free toolbox for uncertainty quantification and management.
This MATLAB code implements the classical Monte Carlo method for solving partial differential equations (PDEs). The code uses the log function of the norm of a random vector as an example PDE and computes the solution at time T=1 and initial condition x0=0.
Electrical Components Tolerance Analysis Using Matlab
Monte Carlo used for the seminar Monte Carlo Methods in Econometrics and Finance at the university of Copenhagen
ARBO is a package for simulation and analysis of arbovirus nonlinear dynamics.
A sensitivity toolbox that is tailored to the design process in the presence of uncertainties
Channel Modelling using Monte Carlo Simulation for UWOCs
Computationally efficient equalization implementation for known channel. The equalizer works in the frequency domain.
Estimating unknown static channel coefficients on a communication system utilizing Maximum Likelihood Single-Shot Estimation algorithm.
Channel estimations based on RLS, LMS and ML methods.
Sample scripts for signal analysis and error evaluation
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