Projects
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| Reference Number | EP/Z000653/1 | |
| Title | Real-time inversion using self-explainable deep learning driven by expert knowledge | |
| Status | Started | |
| Energy Categories | Other Cross-Cutting Technologies or Research 30%; Not Energy Related 70%; |
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| Research Types | Training 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 70%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 30%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Dr K G van der Zee No email address given Mathematical Sciences University of Nottingham |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 September 2024 | |
| End Date | 31 August 2028 | |
| Duration | 48 months | |
| Total Grant Value | £260,676 | |
| Industrial Sectors | ||
| Region | East Midlands | |
| Programme | UKRI MSCA | |
| Investigators | Principal Investigator | Dr K G van der Zee , Mathematical Sciences, University of Nottingham (100.000%) |
| Web Site | ||
| Objectives | ||
| 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 | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 02/10/24 | |
