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Physics-constrained adaptive learning for multi-physics optimization

Project description

Physics-aware machine learning for fluid mechanics

The ability of fluid mechanics modelling to predict the evolution of a flow is enabled both by physical principles and empirical approaches. On the one hand, physical principles (for example conservation laws) are extrapolative – they provide predictions on phenomena that have not been observed. On the other hand, empirical modelling provides correlation functions within data. Artificial intelligence and machine learning are excellent at empirical modelling. The EU-funded PhyCo project will combine physical principles and empirical modelling into a unified approach: physics-constrained data-driven methods for multi-physics optimisation. The machine learning solutions will not violate physical constraints. The computational framework will be applied to reconstruct high-resolution flow images from low-resolution data; minimise aeroengine emissions with hydrogen-based reacting flows; and maximise zero-emission energy harvesting from fluid-structure oscillations.

Call for proposal

ERC-2020-STG
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Host institution

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
Address
South Kensington Campus Exhibition Road
SW7 2AZ London
United Kingdom

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Activity type
Higher or Secondary Education Establishments
EU contribution
€ 1 500 000

Beneficiaries (2)

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
United Kingdom
EU contribution
€ 1 500 000
Address
South Kensington Campus Exhibition Road
SW7 2AZ London

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Activity type
Higher or Secondary Education Establishments
THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE

Participation ended

United Kingdom
EU contribution
€ 0
Address
Trinity Lane The Old Schools
CB2 1TN Cambridge

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Activity type
Higher or Secondary Education Establishments