Anagrafe della ricerca

In-Deep - Real-time inversion using self-explainable deep learning driven by expert knowledge

Durata:
48 mesi (2024 - 2028)
Responsabile scientifico:
Tipo di progetto:
Ricerca UE - HE - Excellent Science - MSCA
Ente finanziatore:
COMMISSIONE EUROPEA (COMMISSIONE EUROPEA)
Codice identificativo progetto:
101119556
Ruolo PoliTo:
Partner

Abstract

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.

Persone coinvolte

Dipartimenti coinvolti

Partner

  • AGH Akademia Gorniczo Hutnicza im Stanislawa Staszica
  • BCAM- Basque Center for Applied Mathematics
  • ENSAM - ECOLE NATIONALE SUPERIEURE DES ARTS ET METIERS
  • FUNDACION CURSOS DE VERANO UPV/EHU - Coordinatore
  • FUNDACION TECNALIA RESEARCH & INNOVATION
  • POLITECNICO DI TORINO
  • SIEMENS INDUSTRY SOFTWARE S.R.L.
  • UNIVERSITA' DEGLI STUDI DI PAVIA
  • UNIVERSITY OF NOTTINGHAM
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Parole chiave

Settori ERC

PE7_3 - Simulation engineering and modelling
PE6_12 - Scientific computing, simulation and modelling tools
PE6_7 - Artificial intelligence, intelligent systems, multi agent systems
PE1_17 - Numerical analysis

Obiettivi di Sviluppo Sostenibile (Sustainable Development Goals)

Obiettivo 7. Assicurare a tutti l’accesso a sistemi di energia economici, affidabili, sostenibili e moderni|Obiettivo 11. Rendere le città e gli insediamenti umani inclusivi, sicuri, duraturi e sostenibili|Obiettivo 3. Assicurare la salute e il benessere per tutti e per tutte le età

Budget

Costo totale progetto: € 2.046.614,40
Contributo totale progetto: € 2.046.614,40
Costo totale PoliTo: € 259.437,60
Contributo PoliTo: € 259.437,60