• United Kingdom

Accepting PhD Students

PhD projects

1) Reliable and efficient predictive maintenance tools under uncertainty; 2) Reliable digital simulation tools for improved resilience in Nuclear Power Plants 3) Robust simulation of tritium production in nuclear fusion power plant 4) Resilience Assessment of Interconnected Networks and Smart Infrastructures 5) Human error and Intelligent and Autonomous systems

20142022
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Personal profile

Personal Statement

In oder to achieve a step change in the way the design, development and maintains complex structures and systems it is necessary to effectively characterize and quantify the unavoidable uncertainties. This allows to take riks-informed decision, design robust and resilient systems able to cope with the current challenges. 

The quantification of uncertainty and the design of efficient and resilient systems capable of recovering from catastrophe is a requirement of numerous industries. Often researchers and practitioners in different disciplines use different languages to describe essentially similar techniques. There is a need of  effective strategies and technologies that respond to the current and future needs of our society, and build resilience across the wide range of the industrial sectors. 

By adopting virtual engineering frameworks (e.g. digital twin) and exploiting the capability of efficient and reliable computational strategies supported by machine learning and artificial intelligence we can now understand the behavior of complex real world problems, effectively design maintain and managing them. reducing the overall risks involved.

I am working on computational science and engineering since more than 20 years. I started developing stochastic models for the assessment of disposal of radioactive waste. Then, I have developed efficient simulation techniques for high fidelity analysis and reliability analysis of computational expensive model and complex systems with applications in different sectors including Civil, Nuclear and Aerospace. I have a deep understand and knowledge of the current challenges (and opportunities) in computation and simulation techniques and the need for verified and reliable tools.

I am working in a truly multidisciplinary environment in collaboration with members from all the university faculties and strong link with world leading scholars across the world. I am the Chair of the Technical Committee on Simulation for Safety and Reliability Analysis of the European Safety and Reliability Association (ESRA) and Chair of the Technical Committee for the 2019 European Safety and Reliability Conference (ESREL) conference. I am working actively with UK industry including Wood, Airbus, ARUP, EdF UK, AWE,  CCFE and National Nuclear Laboratory. 

 In order to translate the recent development to practicians and industry we are continuosly developing a suite of computational tools:  \href{https://cossan.co.uk}{COSSAN software},

Education/Academic qualification

Doctor of Engineering, Politecnico di Milano

Keywords

  • risk analysis
  • Uncertainty
  • Resilience Engineering
  • Simulation
  • Machine Learning
  • System Modelling and Simulation
  • Reliability
  • Human Factors

Fingerprint Dive into the research topics where Edoardo Patelli is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Reliability analysis Engineering & Materials Science
Uncertainty Quantification Mathematics
System Reliability Mathematics
Accidents Engineering & Materials Science
Model Updating Mathematics
Multi-state System Mathematics
Turbines Engineering & Materials Science
Complex Systems Mathematics

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2019 2022

Research Output 2014 2020

  • 47 Article
  • 3 Conference contribution book
  • 2 Paper
1 Citation (Scopus)

Robust propagation of probability boxes by interval predictor models

Sadeghi, J., de Angelis, M. & Patelli, E., 31 Jan 2020, In : Structural Safety. 82, 10 p., 101889.

Research output: Contribution to journalArticle

Sampling
Uncertainty
17 Citations (Scopus)

A multivariate interval approach for inverse uncertainty quantification with limited experimental data

Faes, M., Broggi, M., Patelli, E., Govers, Y., Mottershead, J., Beer, M. & Moens, D., 1 Mar 2019, In : Mechanical Systems and Signal Processing. 118, p. 534-548 15 p.

Research output: Contribution to journalArticle

Open Access
File
Availability
Markov processes
Uncertainty

Activities 2019 2019

  • 1 Editorial board member

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A - Civil Engineering (Journal)

Edoardo Patelli (Peer reviewer)
1 May 2019

Activity: Publication peer-review and editorial work typesEditorial board member