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
🔥🔥🔥Latest Papers, Codes on Uncertainty-based RL
🔎Official code for our paper: "VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation".
Tools for uncertainty propagation and measurement unit conversion — Outils pour la propagation des incertitudes et la conversion d'unités de mesure
Estimate Crop Models' parameters
VARS-TOOL sensitivity analysis package in Python
PyTorch implementation of Probabilistic MIMO U-Net
A toolbox for the calibration and evaluation of simulation models.
Uncertain Shapelet Transform Classification, a shapelet method for uncertain time series classification
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)
R package: spup - Spatial Uncertainty Propagation Analysis
Python library and command line tool performing the Transient Scanning Technique by Brouwer et al.
A comprehensive research platform for Physics-Informed Neural Networks (PINNs) featuring 10+ applications including forward/inverse problems, data assimilation, and scientific discovery with interactive web interface.