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Mean-field Approaches, optimal TRansport and artificial Intelligence: a mathematical framework for compleX systems

Project description

New frameworks for modelling chaotic real-world networks

Our most vital everyday systems like power grids, banking networks and traffic systems are deeply connected, which renders it difficult to model them when massive events (a sudden market crash or a blackout) appear, throwing everything into chaos. Supported by the Marie Skłodowska-Curie Actions programme, the MATRIX project will introduce a new mathematical framework to tackle limitations in current models that cannot still fully capture these real-world uncertainties. Researchers will design reinforcement-learning algorithms to calculate optimal controls when shared, simultaneous shocks disrupt all agents at once. Secondly, they will establish rigorous foundations using adapted Wasserstein distances to guarantee system stability while strictly respecting causal timelines. Ultimately, physics-informed neural networks will approximate solutions for severe, discontinuous disruptions.

Objective

Modern societies rely on interconnected systems in which a large number of complex agents interact amid uncertainty and systemic shocks. In recent years, the mathematical analysis of such stochastic systems has advanced in three closely related areas: mean-field games (MFGs), McKean-Vlasov systems and stochastic partial differential equations (SPDEs). However, multiple roadblocks still prevent the rigorous treatment of intricate real-life problems, such as households in a smart grid, banks in a network, or autonomous vehicles in a city.

The MATRIX project (Mean-field Approaches, optimal TRansport and artificial Intelligence: a mathematical framework for compleX systems) aims to address the limitations of the current models by accounting for shared randomness (events affecting all agents simultaneously, such as policy changes or extreme weather), causal consistency (to prevent information leakage), and non-Gaussian dynamics (such as outages or crashes). A special emphasis will be placed on systems incorporating neural-network-driven agents and on their interaction at the mean-field limit.

The proposed research is organised into three mutually reinforcing work packages (WPs). WP1 involves constructing reinforcement-learning algorithms for MFGs driven by common noise; the aim is to produce scalable representations of optimal controls under shared shocks. WP2 focuses on establishing rigorous foundations for adapted Wasserstein distances in path-dependent McKean-Vlasov systems, providing quantitative stability and propagation-of-chaos results that respect causality. WP3 tackles the well-posedness of McKean-Vlasov SPDEs with Lévy noise and develops physics-informed neural networks that can approximate the underlying solutions despite discontinuities.

The MATRIX project will strengthen the capability to model, predict and control modern complex systems, supporting strategic objectives related to green-energy transition, financial stability and autonomous systems.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2025-PF

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Coordinator

CARDIFF UNIVERSITY
Net EU contribution
€ 276 187,92
Address
RESEARCH SERVICES C/O MAIN BUILDING
CF10 3AT CARDIFF
United Kingdom
Region
Wales East Wales Cardiff and Vale of Glamorgan
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data