2025-10-02 –, Robert Faure Amphitheater
Language: English
This talk introduces UncertaintyQuantification.jl , a generalised framework for uncertainty quantification. The framework has undergone extensive development since its initial release in August of 2020 and now includes a number of numerical algorithms to quantify and propagate uncertainties. We have previously presented the package at two scientific conferences and now want to share it with the wider Julia community.
In this talk we discuss a significant subset of the features currently available. Adequate illustrative numerical examples from various engineering disciplines are presented throughout, to highlight the capabilities of the implemented algorithms.
The definition of uncertainty follows from the absence of certainty describing a state of absolute knowledge where everything there is to know about a process is known. This however is a theoretical and unachievable state, rendering deterministic models insufficient for safe analysis of engineering systems.
With UncertaintyQuantification.jl we aim to provide a comprehensive and generalised toolbox to deal with uncertainty across various engineering disciplines.
Current features:
- Simulation-based reliability analysis
- (Advanced) Monte Carlo simulation
- Quasi Monte Carlo
- Line Sampling
- Subset Simulation
- Global Sensitivity analysis
- Sobol indices
- Metamodeling
- Response Surface Methodology
- Polynomial Chaos Expansion
- Bayesian Updating
- Metropolis-Hastings
- Transitional Markov Chain Monte Carlo
- Third-party solvers
- Connect to any solver by injecting random samples into source files
- HPC interfacing with slurm
- Stochastic Dynamics
- Power Spectral Density Estimation
- Stochastic Process Generation
- Imprecise probabilites
- Intervals
- Probability boxes
