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IN-DEEP

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Real time inversion using self explainable deep learning driven by expert knowle...

Real time inversion using self explainable deep learning driven by expert knowledge IN-DEEP is a European Doctoral Network composed of nine doctoral candidates (DCs) and top scientists with complementary areas of expertise in applied mathematics, artificial intelligence, high-performance computing, and engineerin... ver más

31/01/2028

UPV

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

UNIVERSIDAD DEL PAIS VASCO/EUSKAL HERRIKO UNI... No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 44

Fecha límite participación Sin fecha límite de participación.

Ver 9 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-06-13

HORIZON-MSCA-2022-DN-01-01: MSCA Doctoral Networks 2022 Expected Outcome:Project results are expected to contribute to the following outcomes:

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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

9 Participantes

UPV

251.97K€ | Lider

POLITO

259.44K€ | Participante

THE UNIVERSITY OF NOTTINGHAM

N.D. | Participante

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Duración del proyecto: 55 meses Fecha Inicio: 2023-06-13
Fecha Fin: 2028-01-31

Líder del proyecto

UNIVERSIDAD DEL PAIS VASCO/EUSKAL HERRIKO UNI... No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 44

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto IN-DEEP is a European Doctoral Network composed of nine doctoral candidates (DCs) and top scientists with complementary areas of expertise in applied mathematics, artificial intelligence, high-performance computing, and engineering applications. Its main goal is to provide high-level training to the nine DCs in designing, implementing, and using explainable knowledge-driven Deep Learning (DL) algorithms for rapidly and accurately solving inverse problems governed by partial differential equations (PDEs). Inverse problems in which the unknown parameters are connected to experimental measurements through PDEs cover from medical applications - like cancer growth assessment - to the safety of civil infrastructures, and green geophysical applications such as geothermal energy production. Their application value is measured in human lives and society's well-being, which goes beyond any quantifiable amount of money. This is why equipping a new generation of specialists with highly-demanded skills for the upcoming transition toward safe and robust AI-based technologies is imperative. Despite the promising results in many applications, DL for PDEs has severe limitations. The most troublesome is its lack of a solid theoretical background and explainability, which prevents potential users from integrating them into high-risk applications. IN-DEEP aims to remove these constraints to unleash the full potential of DL algorithms for PDEs. We will achieve this by: (a) focusing on emerging applications of DL for PDEs with immense societal and/or industrial value, (b) designing mathematics-infused advanced solvers to address them efficiently, and (c) involving, from the beginning, industrial and technological agents which can monitor, upscale, and exploit this knowledge. On the way, we shall establish the foundations of a better knowledge exchange ecosystem amongst the main academic and industrial actors within Europe, disseminating the results worldwide.

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