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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
See other projects for this call

Funding Scheme

ERC-STG - Starting Grant

Host institution

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
Address
Trinity Lane The Old Schools
CB2 1TN Cambridge
United Kingdom
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 1 500 000

Beneficiaries (1)

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
United Kingdom
EU contribution
€ 1 500 000
Address
Trinity Lane The Old Schools
CB2 1TN Cambridge
Activity type
Higher or Secondary Education Establishments