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This code base is intended to serve as a starting point for interested researchers or practitioners to extend or apply the uncertainty propagation portion of the author's Master's thesis " GUM-compliant neural-network robustness verification".

This package accompanies my Master's thesis on GUM-compliant neural network robustness verification and provides an implementation using PySCIPOpt.

This codebase provides convenient access to the annotated data set of one electromechanical cylinder at ZeMA testbed.

A simple extension to deep ensembles for an improved uncertainty quantification

In this repository, a Deep-learning model is given, which was trained to extract the vertical position of the gas-liquid-interface over time (liquid level time series) from video recordings of horizontal gas-liquid pipe flow experiments.

This project contains the implementation and examples of the publication MM/GW, CE, "A rejection Sampler extending GUM-S1", Metrologia, 2021 [TODO].

Slides from the talk about Data Analysis Using Modern Python