Descripción del proyecto
Algoritmos de aprendizaje profundo para problemas inversos
Los problemas inversos utilizan ecuaciones diferenciales parciales (EDP) para establecer vínculos entre parámetros desconocidos y datos experimentales. Tienen amplias aplicaciones, como evaluar la evolución del cáncer, garantizar la seguridad de las infraestructuras civiles y mejorar la producción de energía geotérmica. No obstante, los métodos de aprendizaje profundo (DL, por sus siglas en inglés) para las EDP presentan limitaciones, en particular la falta de fundamentos teóricos y de interpretabilidad, lo que impide su integración en aplicaciones de alto riesgo. En el proyecto IN-DEEP, financiado por las MSCA, se reúne a doctorandos y científicos con el objetivo de formar a posgraduados en la creación, implementación y aprovechamiento de algoritmos de aprendizaje profundo basados en el conocimiento. El objetivo de estos algoritmos es abordar con rapidez y precisión los problemas inversos planteados por las EDP. El equipo de IN-DEEP se ha comprometido a desarrollar soluciones avanzadas basadas en DL para las EDP de gran relevancia social e industrial.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
Programa(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régimen de financiación
HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral NetworksCoordinador
48940 Leioa
España