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.

Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.

Error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical integration.

Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.

KFNet: Learning Temporal Camera Relocalization using Kalman Filtering (CVPR 2020 Oral)

Multi Task Learning Implementation with Homoscedastic Uncertainty in Tensorflow

Numerical derivatives for Python

Model for learning document embeddings along with their uncertainties

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

This repository provides a framework to perform two-stage stochastic programming on a district energy system considering uncertainties in energy demands, solar irradiance, wind speed, and electricity emission factors.

GUNDAM, for Generalized and Unified Neutrino Data Analysis Methods, is a suite of applications which aims at performing various statistical analysis with different purposes and setups.

C++ header library for first- and second-order uncertainty propagation

Generates chemical kinetics data.

Modeling pandemics subject to uncertainties

ADELE is a dataset of vibration in a beam that exhibits nonlinear behavior even in the healthy condition, and it is exposed to a type of damage that causes the structure to display a nonlinear behavior with a different nature than the initial one. This experiment was conducted by the SHM lab at the University of California San Diego with collabo…

My contributions to the #30DayChartChllenge, #rstats edition

Uncertainty propagation in C++

Constants library for physics and chemistry Based off of NIST and CODATA

Python package for uncertainty calculations and error propagation.

Measurements.jl compatibility layer for UnitSystems.jl