Korali: Efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization

Bayesian Framework for Multidisciplinary Uncertainty Quantification and Optimization

This paper presents a comprehensive methodology that combines uncertainty quantification, propagation and robustness-based design optimization using a Bayesian framework. Two types of epistemic uncertainty regarding model inputs/parameters are emphasized: (1) uncertainty modeled as p-box, and (2) uncertainty modeled as interval data. A Bayesian approach is used to calibrate the uncertainty mode...

Forward and Backward Uncertainty Quantification in Optimization

This contribution gathers some of the ingredients presented during the Iranian Operational Research community gathering in Babolsar in 2019.It is a collection of several previous publications on how to set up an uncertainty quantification (UQ) cascade with ingredients of growing computational complexity for both forward and reverse uncertainty propagation.

Joint Bayesian Stochastic Inversion of Well Logs and Seismic Data for Volumetric Uncertainty Analysis

Here in, an application of a new seismic inversion algorithm in one of Iran’s oilfields is described. Stochastic (geostatistical) seismic inversion, as a complementary method to deterministic inversion, is perceived as contribution combination of geostatistics and seismic inversion algorithm. This method integrates information from different data sources with different scales, as prior informat...

An Efficient Computational Framework for Uncertainty Quantification in Multiscale Systems

A Bayesian Framework for Quantifying Uncertainty in Stochastic Simulation

When we use simulation to estimate the performance of a stochastic system, the simulation often contains input models that were estimated from real-world data; therefore, there is both simulation and input uncertainty in the performance estimates. In this paper, we provide a method to measure the overall uncertainty while simultaneously reducing the influence of simulation estimation error due ...