Project description
Probabilistic neural network for scientific applications
Machine learning has driven significant advances across multiple scientific fields and sectors. It has also enabled the development of improved methodologies and equipment, which is crucial given scientists’ need for better analysis tools. However, despite its potential, current large-scale neural networks are unsuitable for scientific applications involving limited datasets. Supported by the Marie Skłodowska-Curie Actions programme, the PRINN project will develop a breakthrough in machine learning for scientific use: a physics-informed probabilistic neural network specifically designed for small datasets. The project will research and address challenges limiting current probabilistic methods, thereby enhancing the speed and accuracy of these networks.
Objective
Scientists have huge needs for better analysis tools. Machine learning is a powerful framework to provide outstanding tools, but the current large-scale neural networks are not adapted for scientific applications where datasets are limited. Probabilistic networks are a powerful alternative for smaller datasets. However, they have flaws that prevent them to work well for complex tasks. They notably have speed and accuracy limitations.
This project aims to make a breakthrough in machine learning for scientific applications by developing a new physics-informed probabilistic neural network adapted for small datasets. The basic unit of our network overcomes the limitations of the current probabilistic methods by considering a recurrent Gaussian process and using an analytical integration method. The first objective of our project is to deploy several units that each represent a state in a neural network, and to consider abrupt transitions from one state to another. This network will be applied to the analysis of single-particle tracking data, an important and complex biology problem for which our model will be particularly well-suited. Next, we will extend our network to consider maps and spatiotemporal maps. This second phase will be applied to mapping cell viscosity, a particularly promising super-resolution technique that does not require specific markers. Our last objective is to create a modular network to enable better scalability of our architecture for more complex tasks. We will test this architecture on multimodal data like vital signs to predict patient outcomes. This project will therefore result in a powerful open-access neural network that other scientists will be able to derive for their scientific applications, along with a series of three scientific tools that will redefine the state-of-the-art in their respective fields and for which we expect a large use.
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.
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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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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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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.
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships
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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) HORIZON-MSCA-2024-PF-01
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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.
75006 Paris
France
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.