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Projects: Projects for Investigator
Reference Number EP/Y005619/1
Title Real-time digital optimisation and decision making for energy and transport systems
Status Started
Energy Categories Other Cross-Cutting Technologies or Research(Energy Models) 15%;
Other Cross-Cutting Technologies or Research(Energy system analysis) 10%;
Renewable Energy Sources(Wind Energy) 25%;
Energy Efficiency(Transport) 25%;
Hydrogen and Fuel Cells(Hydrogen) 25%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 40%;
PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 40%;
ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 10%;
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 10%;
UKERC Cross Cutting Characterisation Not Cross-cutting 60%;
Systems Analysis related to energy R&D (Other Systems Analysis) 20%;
Sociological economical and environmental impact of energy 20%;
Principal Investigator Dr G Rigas

Imperial College London
Award Type Standard
Funding Source EPSRC
Start Date 01 May 2023
End Date 31 March 2025
Duration 23 months
Total Grant Value £1,414,614
Industrial Sectors Energy; Transport Systems and Vehicles
Region London
Programme Technology Missions Fund
Investigators Principal Investigator Dr G Rigas , Aeronautics, Imperial College London (99.997%)
  Other Investigator Dr A Borovykh , Mathematics, Imperial College London (0.001%)
Dr S Laizet , Aeronautics, Imperial College London (0.001%)
Dr L Magri , Aeronautics, Imperial College London (0.001%)
  Industrial Collaborator Project Contact , Atkins (0.000%)
Project Contact , NVIDIA Corporation, USA (0.000%)
Project Contact , Engys Ltd (UK) (0.000%)
Project Contact , Catesby Projects (0.000%)
Web Site
Abstract In this project, we will seamlessly combine two disciplines that have been historically received continuous government and industrial funding: physics-based modelling, which is generalisable and robust but may require tremendous computational cost, and machine learning, which is adaptive and fast to be evaluated but not easily generalisable and robust. The intersection of the two spawns scientific machine learning, which maximises the strengths and minimises the weaknesses of the two approaches. The data will be provided by high-fidelity simulations and experiments, from the UK state-of-the-art facilities and software. The efficiency of the machine learning training will be maximised for the algorithms to require minimal energy (thereby, producing minimal emissions by minimising electricity consumption). This project builds upon large UK and EU funded expertise in scientific machine learning and simulation, which will be generalised to fast, real-time decision making. The most significant bottleneck of most scientific machine learning is that they need time to be re-trained offline when new data becomes available. We will transform offline paradigms into real-time approaches for the models to re-adapt and provide accurate estimates on the fly. This project will culminate into the delivery of practical digital twins (defined as digital counterparts of real world physical systems or processes that can be used for simulation, prediction of behaviour to inputs, monitoring, maintenance, planning and optimisation) to solve currently intractable problems in wind energy, hydrogen, and road transportation. This project will transfer the technical achievements and real-time digital twin to policy-making
Publications (none)
Final Report (none)
Added to Database 14/06/23