Risk and Uncertainty Quantification
I am focus especially on mitigating critical threats to engineering performance, such as those presented by natural and technical hazards, extreme events and human error. This work is based on comprehensive risk and uncertainty quantification methods developed in-house. These use probabilistic, interval and fuzzy methods, often combined with imprecise probabilities techniques.
The quantification of uncertainties is a key requirement and challenge across various disciplines in order to operate systems of diverse nature safely under the evolutionary dynamics of input and boundary conditions and to assess risks realistically. Emphasis are on the implementation of efficient quantification tools in engineering analyses and the evaluation of the associated results in view of engineering decision making.
The aim it to provide a systematic understanding of nuclear power engineering and to provide a comprehensive and broad overview of contemporary issues and applications associated with nuclear power engineering.
Uncertainty Quantification on HPC
In collaboration with the Institute for Risk and Uncertainty and STFC Hartree Centre, we are offering a 3 days training course on Uncertainty Quantification using COSSAN Software on High Performance Computing.
More details are available hereTraining Event @ Hartree Centre
Structure of the training programme
Each day focuses on a specific topic. This allows the participant to attend a specific training day. The first day is dedicated to an introduction of general concepts of stochastic and probabilistic analysis as well as an introduction to High Performance Computing with practical exercises. The second day is dedicated to COSSAN-X software while the last day will concentrate on the OpenCossan software.
Aims and Learning outcomes
You will learn the main techniques available for dealing with Risk and Uncertainty and how to use High Performance Computing to speed up the analysis.
Main Concepts and techniques
Random Variables and Random Variables Sets
Monte Carlo simulation and advanced simulation techniques (Subset simulation, Line Sampling, Importance Sampling, Latin Hypercube Sampling)
Global and Local Sensitivity analysis
Global optimization techniques
Surrogate Models (Artificial Neural Networks, Response surface, Kriging)
Reliability based and robust design