Project description
Deep learning helps scientists delve under the surface
Beneath the surface of the earth, rock is subjected to many forces and stresses that can induce fractures. This creates subsurface fracture networks with intricate and interconnected geometries through which water and other liquids and gases can flow. Given the interest in subsurface energy storage related to fuels, including molecular hydrogen or natural gas, modelling flow in subsurface fracture networks is an important goal. It has been quite challenging to reach in a way that enables the required accuracy without an extremely high computational cost. With the support of the Marie Skłodowska-Curie Actions programme, the MiDiROM project will develop deep learning enhanced reduced-order modelling techniques for mixed-dimensional flow problems to address this issue.
Objective
Exploiting the subsurface as an energy storage site is a crucial step to meet some of the challenges arising from energy production by renewable sources. For such applications, a proper understanding of the subsurface flow is essential and calls for efficient and effective computational models. The main difficulties in the mathematical modeling arise from the highly varying material parameters as well as the presence of fracture networks, the latter aspect being crucial due to its leading impact on flow characteristics. These features are a leading source of computational complexity, often making it infeasible to use full order simulation models in real-life situations, particularly when there is the need to investigate different scenarios and/or quantify uncertainties.
In this project, I will build on my acquired expertise in mixed-dimensional models of fractured porous media, where fractures are represented as a collection of immersed, lower-dimensional manifolds. Although these models lead to accurate numerical methods, the computational cost remains impractically high. To overcome this, I propose to develop reduced order models for mixed-dimensional flow problems. In particular, I will investigate how to properly capture non-linear dependencies on model parameters such as the fracture network configuration by extending and adapting the deep learning enhanced reduced order modeling techniques recently investigated by researchers of the host institution.
The combination of research fields is reflected by the composition of the project: the proponent has a strong theoretical background in analyzing and discretizing mixed-dimensional models whereas the supervisor and associated host institute are leading experts in fractured porous media flow and application-driven reduced order modeling. Additionally, the host institution offers the necessary research and complementary skill training for the proponent to further develop and thrive as an independent researcher.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences mathematics applied mathematics numerical analysis
- natural sciences mathematics applied mathematics mathematical model
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
20133 Milano
Italy
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.