This package provides an implementation of the Metropolis algorithm for simulating spin configurations.
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Updated
Jun 2, 2024 - Python
This package provides an implementation of the Metropolis algorithm for simulating spin configurations.
OpenMC Monte Carlo Code
GemPy is an open-source, Python-based 3-D structural geological modeling software, which allows the implicit (i.e. automatic) creation of complex geological models from interface and orientation data. It also offers support for stochastic modeling to address parameter and model uncertainties.
This strategy works for every market condition irrespective of the movement
6DOF Flight Simulation for High Power Model Rocketry
Plotting and analysis tools for ARTIS simulations
Performed model evaluation using evaluation metrics such as accuracy, precision, recall, F1-score etc. Then model interpretation using feature importance, SHAP and LIME. Finally , evaluated model robustness and stability through techniques like bootstrapping or Monte Carlo simulations.
Blackjack Notebook (bjnb): Probabilistic analysis and simulation
Structure for organizing Monte Carlo simulations in R
Risk analysis of greenhouse gas projects using monte carlo simulations.
Pure python library for weighting IceCube simulation
Molecular dynamics and Monte Carlo soft matter simulation on GPUs.
CRPropa is a public astrophysical simulation framework for propagating extraterrestrial ultra-high energy particles. https://crpropa.github.io/CRPropa3/
Powerful, efficient particle trajectory analysis in scientific Python.
SimPET is a framework intended to setup and launch PET imaging Monte Carlo simulations on a simple way. It uses popular tools such as SimSET and STIR
Python (and C++) interface to PartMC with Jupyter/Python, Julia and Matlab examples
UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.
Python implementation of the cellular automata model corresponding to Lange, Schmied et. al.
A small application demonstrating the central limit theorem using a Monte Carlo simulation
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