Project description
AI-powered solutions to simulate dynamic system behaviour
AI is fundamentally changing how we solve complex differential equations that describe everything from natural phenomena to technological systems. Neural operators, a breakthrough in AI, can tackle these equations with remarkable speed and accuracy compared to traditional methods that are computationally intensive and therefore impractical for real-time applications. With the support of the Marie Skłodowska-Curie Actions programme, the PANDA project plans to develop such AI frameworks powered by universal neural operators that can capture multiscale and non-linear interactions in physical phenomena. These tools will enable accurate predictions of system behaviour across various partial differential equation families under dynamic conditions. Researchers will fine-tune AI-based PDE solvers to model technological systems, including forecasting mobile network traffic and user mobility.
Objective
The resolution of Partial Differential Equations (PDEs) is fundamental to complex system modelling across a broad range of scientific disciplines, including physics, biology, and engineering. Conventionally, PDEs are solved through numerical methods, which are invariably computationally intensive, thus curbing the adoption of these techniques in intricate problems and real-time applications. Recently, artificial intelligence (AI)-driven approaches have emerged as promising alternatives to approximate with remarkable speed and accuracy, the solution of physics-based PDEs, and ultimately supplant legacy numerical PDE solvers.
This action will explore the development of AI-powered frameworks for the resolution of a wide range of physics-based PDEs. These frameworks will be underpinned by universal neural operators that can capture multi-scale and non-linear interactions present in physical phenomena, thereby enabling accurate predictions of physical system behaviour even under dynamic conditions and different families of PDEs.
Building on this foundation, the AI-based PDE solvers will be fine-tuned and leveraged to emulate the dynamics of technological non-physical systems. Specifically, they will be employed to forecast the spatiotemporal mobile network traffic demands and user mobility, with the respective traffic demand and mobility PDEs being mined in a data-driven manner. The proposed approach will be validated using real-world data from an operating mobile network, demonstrating the capacity of AI-based PDE solvers to simulate human-made system behaviours.
Ultimately, this action will instate the potential of AI in solving both classical physics-based and modern data-driven PDEs, offering a novel perspective on the intersection of physics-driven AI and next-generation digital twin modelling. At the same time, it will mold a multi-dimensional researcher and equip them with skills essential to pivot into the next generation of scientific and academic leaders.
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
- engineering and technology electrical engineering, electronic engineering, information engineering information engineering telecommunications telecommunications networks mobile network
- natural sciences mathematics pure mathematics mathematical analysis differential equations partial differential equations
- natural sciences mathematics applied mathematics numerical analysis
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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)
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Topic(s)
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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-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-PF
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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.
8092 Zuerich
Switzerland
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