IN-DEEP, the new MSCA Doctoral Network project to train doctoral students in Deep Learning techniques

Bizkaia, News

The project is coordinated by the researcher David Pardo (UPV/EHU) and the coordination from BCAM is led by Judit Muñoz Matute, postdoctoral researcher.

The aim is to promote training and research in new deep learning technologies for inverse problems.

The IN-DEEP (Real-time inversion using self-explainable deep learning driven by expert knowledge) project has been awarded the highly competitive MSCA Doctoral Network (DN) grant. The project is endowed with 2.3 million euros to train and supervise highly qualified doctoral students through a consortium of universities and companies from different research areas and sectors in the European Union.

For BCAM’s project coordinator, Judit Muñoz Matute, “as BCAM’s principal investigator in the project, it is a unique opportunity (and a challenge) to be able to supervise my first PhD students and thus build a solid career as an independent senior researcher”, says the postdoctoral researcher in the Mathematical Design, Modellig and Simulation research line at BCAM.

The main objective of the IN-DEEP project is to provide high-level training to nine PhD students in the design, implementation and use of explainable knowledge-based deep learning algorithms to quickly and accurately solve inverse problems governed by Partial Differential Equations (PDE).

This area of research has experienced a worldwide growth in the last decades. This has been motivated by promising results in many applications. The project will focus on real-world, high-risk problems arising from applications related to geophysics, smart cities and health. IN-DEEP will develop fundamental research in universities and research institutes that will be validated and applied to real use cases in technology centres and companies. “IN-DEEP will give us the opportunity to train PhD students who will become excellent modern researchers in DL techniques for fundamental inverse problems for our society, with a very complete profile and suitable career prospects in both the academic and non-academic sectors,” adds researcher Judit Muñoz Matute.

Inverse problems in which unknown parameters are connected to experimental measurements using PDEs cover many applications: medical imaging of the human body or cancer growth assessment, safety of civil infrastructures such as bridges and buildings, and ecological geophysical applications such as underground hydrogen and CO2 storage or geothermal energy production.

This project is also challenging because, despite promising results in many applications, DL for EDPs currently has severe limitations. “The most problematic is their lack of a solid theoretical basis and explainability, which prevents potential users from integrating them into high-risk applications. Therefore, it is an opportunity for the project consortium to generate results at the frontier of knowledge, developing cutting-edge technologies from a multidisciplinary and cross-sectoral perspective,” concludes Muñoz Matute, BCAM’s main project coordinator.

The project consortium is made up of 7 European universities and research centres; University of the Basque Country/ Euskal Herriko Unibertsitatea (Spain) coordinating institution, University of Nottingham (UK), University of Pavia (Italy), Politecnico di Torino (Italy), School of Arts and Crafts (France), AGH University (Poland) and the Basque Centre for Applied Mathematics (BCAM) and two companies Tecnalia Research & Innovations and Siemens, with complementary areas of expertise in applied mathematics, artificial intelligence, high performance computing and engineering applications.


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