Simulation and Digital Twins: challenges and opportunities

NOTE: This event is currently waitlisted. We may be able to increase the number of attendees, so if you are please join the waitlist.

This workshop is generously funded through the 'Celebrating New Appointments Scheme' of the London Mathematical Society and the Mathematical Sciences Research Centre in the School of Mathematics and Physics.

Invited speakers:

Dr Laura Boyle, Queen's University Belfast

Professor Christine Currie, University of Southampton

Dr Thomas Monks, University of Exeter

Dr Luke Rhodes-Leader, Lancaster University

Provisional programme:

12:30 - 13:00 Refreshments

13:00 - 13:10 Welcome and introduction

13:10 - 14:00 Professor Christine Currie 'Making Real-Time Decisions with a Simulation-Based Digital Twin'

14:00 - 14:45 Dr Luke Rhodes-Leader 'Tracking and Detecting Systematic Errors in Digital Twins'

14:45 - 15:30 Tea, coffee, and networking

15:30 - 16:15 Dr Tom Monks 'Towards a Framework for Sharing Tools and Artifacts for Reusable Simulations in Healthcare'

16:15-16:30 Introduction to Virtual Institute for Data Intensive Research

16:30 - 17:00 Dr Laura Boyle 'Simulation and digital twins in healthcare'

17:00 - 18:00 Drinks reception

Accessibility and Venue Facilities:

The event will be held at Riddell Hall at Queen's University, Belfast. Information regarding the accessibility of the venue and relevant facilities can be found here. Private rooms are available for nursing mothers and also for prayer and quiet reflection. If you have any questions regarding the facilities at the venue please do not hesitate to contact the organisers (laura.boyle@qub.ac.uk).

Abstracts:

Professor Christine Currie

Making Real-Time Decisions with a Simulation-Based Digital Twin

Experimentation and optimisation of simulation models has been common practice for several decades. What has changed in recent years is the increased prominence of the idea of a digital twin. While the term digital twin has a variety of definitions dependent on the research area, within the simulation community it gives the promise of using simulation for operational decision-making, returning recommendations within minutes. This also brings in the idea of symbiotic simulation.

The term symbiotic simulation was first coined by Fujimoto et al. (2002) but Aydt et al. (2009) give a very useful definition: “a paradigm in which a simulation system and a physical system are closely associated with each other”. Typically, in a symbiotic simulation system, the simulation model is updated in real-time with data from the physical system and used to carry out an exploration of different possible system configurations. This gives the decision-maker information on the likely outcomes, allowing them to choose the best option.

We will use an example from manufacturing to describe advances in the use of simulation for real-time decision-making using symbiotic simulation. The example draws on a collaboration with an engine manufacturer who were looking to optimise the repair processes on their production line: essentially their problem is one of determining the repair order of machines when more than one is broken simultaneously. The simulation model of the production line is relatively complex and, as a result, takes some time to run. As decisions are needed within minutes, this limits the number of simulation replications that can be made. We use a multi-fidelity simulation optimisation procedure to find a good repair strategy with only a small number of simulation replications.

The talk will end with a discussion of the challenges and open research questions in the area of real-time or operational decision-making via a digital twin or symbiotic simulation framework.

References

Aydt, H., S. J. Turner, W. Cai, and M. Y. H. Low (2009). Research issues in symbiotic simulation. In Proceedings of the 2009 Winter Simulation Conference, ed. M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, and R. G. Ingalls, 1213 – 1222. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.

Fujimoto, R., D. Lunceford, E. Page, and A. U. (editors). 2002, August. Grand challenges for modeling and simulation: Dagstuhl report. Technical Report 350, Schloss Dagstuhl. Seminar No 02351.

Dr Luke Rhodes-Leader

Tracking and Detecting Systematic Errors in Digital Twins

A digital twin is a model of a system that uses real-time data to keep up-to-date with the real system it is modelling. They can be used for real-time decision making and have a wide variety of applications. Some digital twins make use of stochastic simulation, in areas including port operations and manufacturing. The research is usually from an applied or computational stance, with statistical considerations largely ignored. Even with best practices, the DT and the real-world system may become misaligned over time. The comparison, for validation purposes, is not straightforward, especially in stochastic systems. The key issue is that traditional validation methods assume a set of independent and identically distributed real-world performance observations. As the digital twin adapts to the current circumstances, each problem it is asked to solve may be unique. Therefore, the real-world performance will not be indentically distributed, invalidating most methods. In this talk, we presnt a statistical method to detect misalignment between the real world and a digital twin, even though both the simulation and the real-world system are inherently stochastic. An empirical evaluation and a realistic illustration are discussed.