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A Novel and Affordable Multi Fidelity Deep Neural Network Uncertainty Quantifica...

A Novel and Affordable Multi Fidelity Deep Neural Network Uncertainty Quantification Robust Optimization Design Framework for Industrial Turbomachinery "This fellowship aims to train a talented early career researcher and to contribute to the EU scientific excellence by developing an innovative Multi-fidelity dEep neural Network uncerTainty quantificatiOn and Robust optimization... ver más

30/06/2024

IMPERIAL COLLEGE O...

213K€

Presupuesto del proyecto: 213K€

Líder del proyecto

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND ME... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2024-06-30

MSCA-IF-2020: Individual Fellowships

I+D

Cerrada hace 4 años

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No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

IMPERIAL COLLEGE OF SCIENCE...

212.93K€ | Lider

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Duración del proyecto: 38 meses Fecha Inicio: 2021-04-12
Fecha Fin: 2024-06-30

Líder del proyecto

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND ME... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 213K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto "This fellowship aims to train a talented early career researcher and to contribute to the EU scientific excellence by developing an innovative Multi-fidelity dEep neural Network uncerTainty quantificatiOn and Robust optimization design (MENTOR) framework in order to handle the high dimensionality (HD) uncertain problems in the advanced multistage turbomachinery design process. The applicant is a highly dedicated and motivated young researcher and has been stimulated to propose this novel idea. He has been successively honoured with several prestigious awards including the National Scholarship for PhD Candidate, Excellent PhD Graduates of Beijing, Excellent Doctoral Dissertations Award of Beijing and ASME Young Engineer Turbo Expo Travel Award attributed to his excellent research achievement in cost-efficient uncertainty quantification (UQ) studies. The traditional UQ methods can hardly control the computation cost for predicting the higher-order moments of multistage turbomachinery performance considering HD input uncertainties. The deep learning technology is a promising approximator in predicting the HD function. Integration of multi-fidelity (MF) methodology with deep neural network (DNN) can further combine their complementary merits. Thus, the novel MF-DNN method is proposed here and its effectiveness in handling a 60-dimensional test function has been preliminarily validated in the Incoming Researcher's recent work. Through this research fellowship, an affordable MENTOR framework will be finally established to investigate the multi-source uncertainty effects on multistage turbomachinery performance. This project has been carefully designed to match the applicant's profile with the strength of Imperial's UQ Lab, and thus will facilitate excellent two-way knowledge transfer and training activities. Successful completion of this fellowship will contribute to achieving the goal of the EU's ""Green Deal"" and will benefit the applicant's academic career prospect."

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