Photo of Dr Edoardo Patelli

Dr Edoardo Patelli Ph.D

Senior Lecturer Civil Engineering and Industrial Design

Teaching and Learning

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.

Nuclear Engineering
(bbc.co.uk)
(bbc.co.uk)

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


Modules for 2019-20

ANALYSIS OF SAFETY CRITICAL SYSTEMS AND COMPUTATIONAL INFERENCE

Module code: ENGG406

Role: Module Co-ordinator

ASSESSMENT AND COMMUNICATION OF RISK

Module code: RISK622

Role: Module Co-ordinator

Multidisciplinary MRes Research Project

Module code: RISK661

Role: Module Co-ordinator

Quantitative and Qualitative Perspectives of Risk

Module code: RISK623

Role: Module Co-ordinator

RESEARCH TRAINING AND IMPACT

Module code: RISK624

Role: Teaching

RISK & UNCERTAINTY: NUMERICAL APPLICATIONS

Module code: ENGG403

Role: Module Co-ordinator

UNCERTAINTY, RELIABILITY AND RISK 1

Module code: ENGG304

Role: Module Co-ordinator