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 repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.

python modules for model-independent uncertainty analyses, data-worth analyses, and interfacing with PEST(++)

Quantify uncertainty and sensitivities in your computer models with an industry-grade Monte Carlo library.

Robust and optimal design and analysis of linear control systems

Tools for uncertainty propagation and measurement unit conversion — Outils pour la propagation des incertitudes et la conversion d'unités de mesure

VARS-TOOL sensitivity analysis package in Python

Estimate Crop Models' parameters

Uncertain Shapelet Transform Classification, a shapelet method for uncertain time series classification

PyTorch implementation of Probabilistic MIMO U-Net

An in-depth performance profiling library for machine learning models

Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference

This repo contains implementation of uncertainty estimation, rectification, and minimization for guiding the pseudo-label learning in semi-supervised defect segmentation setting.

An interface connecting COMSOL Multiphysics and PEST to solve inverse modelling problems using forward finite element models (paper here: http://doi.org/10.1016/j.cageo.2019.02.001)

Python library and command line tool performing the Transient Scanning Technique by Brouwer et al.

R package: spup - Spatial Uncertainty Propagation Analysis

Some remarks on prior modelling for the basic reproductive number in the Susceptible-Infected-Recovered (SIR) epidemic model

Uncertainty propagation in C++

Clustering algorithms for uncertain data

An overview about PROFOUND code, data, protocols and algorithms for interfacing, calibrating and comparing forest models