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60 seconds with...Domenico DiFrancesco, Alan Turing Institute

Institution News Team

Ahead of his presentation at this year's Simulation and Modelling conference, Domenico highlights some of the issues his work in AI and Risk Management seeks to address and the challenges of translating research into benefits to enhance safety in industry.

For full details of the conference and to book your place, please visit the event website at

Please briefly explain your role, involvement, and experience with simulation and modelling

Domenico DiFrancesco (DDF): I am a Turing research fellow at the Alan Turing Institute. My work is primarily regarding decision making under uncertainty. This requires me to relate engineering models to the underlying decisions that the calculations are intended to support. This can target our analysis in lots of useful ways, including quantifying the expected value of collecting more data. Engineering data often consists of some indirect measurement of a complex physical phenomenon (meaning it has imperfect features), and can also be expensive to obtain. I believe that methods from computational statistics could have a huge benefit to industry in risk-optimal data collection planning.

What is the top challenge facing your industry at present?

DDF: One challenge that I hope engineers become more aware of is assurance and verification. Independent verification of mechanical or structural components is an established part of many industries, but the quality of digital systems can also impact safety. Unverified models and digital twins will expose operators to greater risk. I expect this challenge will become increasingly important as the requirements and complexity of engineering models increase (particularly those with elements of AI). Intelligent methods of assurance and verification need to be developed alongside models to help ensure that engineering sectors can safely integrate and benefit from these new technologies.

How would you say your industry has evolved over the past five years?

DDF: I have spent my last five years in research, so I hope to see our work in data-centric engineering continue to be safely translated into industry. A recent translational project that I worked on with support from my colleagues and industry collaborators is the first independent verification of a digital twin in the maritime industry:

What developments are going on in your industry that may have an impact on the development of future approaches to the use of modelling?

DDF: Novel methods of data collection (such as structural health monitoring) and analysis (scalable Bayesian inference, deep learning) offer much promise to engineering risk management. However, I suggest in my research that these methods still require plenty of thought and engineering judgement. For instance, blindly installing a state of the art sensing system on an old asset missing important design documentation, will not necessarily be a risk-optimal data collection strategy. System effects (inter-dependencies) will remain a challenge requiring combining subject matter expertise with computational statistics.

What will you be presenting at the ‘Simulation and Modelling’ seminar and how will this benefit participants?

DDF: My talk, ‘towards quantifying the expected value of model verification’ will introduce the audience to ideas from uncertainty quantification and decision analysis, with the application of optimising a verification and testing plan for a complex engineering model.

Which other speakers and presentations are you looking forward to hearing at the forthcoming seminar?

DDF: On day 1, I am looking forward to hearing the keynote address from my colleague Adam Sobey (Alan Turing Institute), and he talk on developments in regulation from Rob Turpin (BSI). On day 2, I am especially looking forward to hear from Tim Dodwell (digiLab) on the use of AI and ML in engineering.

Why is it important for engineers and industry to come together at this event and share best practice?

DDF: I believe that events such as this can help us build communities focused on sharing knowledge and ideas. Consider the development of a new standard – I would suggest that engineers can learn from the field of open-source software development. If these documents and all supporting evidence, were publically available, engineers may be able to better engage with and understand the recommendations. Having a formal process for providing feedback (or identifying errors), even from those who are not on standards committees, may help the development and adoption of such guidance.

The Simulation and Modelling 2023 conference will return on 12-13 September 2023 to the MTC in Coventry.

To expand to meet the needs of delegates, the Simulation and Modelling conference 2023 will explore areas relating to AI and machine learning, simulation and sustainability, digital twinning and regulation and standardisation.

Presenting organisations include Jaguar Land Rover, The Alan Turing Institute, Polestar, Digilab, Frazer-Nash Consultancy, HBK UK, Airbus, Red Engineering, JCB, BSI, Williams Advanced Engineering and many more.

For full details of the conference and to book your place, please visit the event website at


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