| EPSRC Reference: |
EP/Y005619/1 |
| Title: |
Real-time digital optimisation and decision making for energy and transport systems |
| Principal Investigator: |
Rigas, Dr G |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Aeronautics |
| Organisation: |
Imperial College London |
| Scheme: |
Standard Research - NR1 |
| Starts: |
01 May 2023 |
Ends: |
31 March 2025 |
Value (£): |
1,414,614
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Energy Efficiency |
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| EPSRC Industrial Sector Classifications: |
| Energy |
Transport Systems and Vehicles |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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28 Mar 2023
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AI for Net Zero
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Announced
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Summary on Grant Application Form |
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.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.imperial.ac.uk |