The Synthesis of Mathematical and Data Driven Modelling of Complex Physical Systems
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The Synthesis of Mathematical and Data Driven Modelling of Complex Physical Systems
CoPED ID
d93f5e5d-b0d1-4dc1-ad9b-0a0e59f07f0a
Status
Closed
Value
No funds listed.
Start Date
Sept. 30, 2018
End Date
Dec. 31, 2022
Description
Forecasting and prediction are operations of critical importance in the energy sector. Whether it is predicting the output of a solar or wind farm, forecasting the energy demand at city and regional levels, predicting the operational condition of assets such as gas turbine plants or the long term environmental impact of engineering operations, they all have to optimize under great uncertainty in a coherent and consistent manner. This proposed PhD posits that an overarching theoretical, methodological and practical framework to integrate both data driven and physics based models will provide greater modelling capability and representation as well as superior predictive capability in the presence of uncertainty.
| Imperial College London | LEAD_ORG |
| Shell (United Kingdom) | STUDENT_PP_ORG |
| Andrew Duncan | SUPER_PER |
| Mark Girolami | SUPER_PER |
Subjects by relevance
- Modelling (representation)
- Forecasts
- Solar wind
- Simulation
- Mathematical models
- Uncertainty
- Solar energy
- Wind energy
Extracted key phrases
- Complex Physical Systems
- Data Driven Modelling
- Long term environmental impact
- Great modelling capability
- Energy sector
- Energy demand
- Engineering operation
- Gas turbine plant
- Superior predictive capability
- Critical importance
- Great uncertainty
- Synthesis
- Wind farm
- Regional level