Descrizione del progetto
Algoritmi di apprendimento profondo per i problemi inversi
I problemi inversi utilizzano equazioni differenziali alle derivate parziali per stabilire collegamenti tra parametri sconosciuti e dati sperimentali. Tali equazioni hanno numerose applicazioni, come la valutazione della crescita del cancro, la verifica della sicurezza delle infrastrutture civili e il miglioramento della produzione di energia geotermica. Tuttavia, gli approcci di apprendimento profondo per le equazioni differenziali alle derivate parziali presentano varie limitazioni, in particolare la mancanza di basi teoriche e di interpretabilità, che ne impediscono l’integrazione in applicazioni ad alto rischio. Il progetto IN-DEEP, finanziato dal programma di azioni Marie Skłodowska-Curie, riunisce dottorandi e scienziati con l’obiettivo di formare laureati nella creazione, nell’implementazione e nello sfruttamento di algoritmi di apprendimento profondo fondati sulla conoscenza intesi a risolvere in modo rapido e accurato i problemi inversi basati sulle equazioni differenziali alle derivate parziali. IN-DEEP è impegnato nello sviluppo di risolutori avanzati basati sull’apprendimento profondo per le equazioni differenziali alle derivate parziali che rivestano una notevole rilevanza a livello sociale e industriale.
Obiettivo
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.
Campo scientifico
- medical and health sciencesclinical medicineoncology
- natural sciencesbiological sciencesecologyecosystems
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesmathematicspure mathematicsmathematical analysisdifferential equationspartial differential equations
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energygeothermal energy
Parole chiave
Programma(i)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Meccanismo di finanziamento
HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral NetworksCoordinatore
48940 Leioa
Spagna