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Partial Differential Equations on Sets of Measures

Project description

Making partial differential equations more practical for real-world applications

Partial differential equations (PDEs) are powerful tools for understanding how systems evolve and interact over time, especially in physics, biology and economics. Despite their widespread use, certain PDE types are difficult to analyse owing to their complexity and the lack of general solutions. The ERC-funded PaDiESeM project aims to study two specific PDEs arising in large-scale systems involving many interacting components. PaDiESeM will additionally strive to further understand PDE stability and behaviour to address practical problems, such as optimising decision-making in competitive environments and managing distributed systems.

Objective

Partial differential equations (PDEs) are ubiquitous in science, whether it is in physics, engineering, biology or economics, as they naturally arise in the modelling of continuous objects. Recently, several PDEs have been derived to characterize complex objects such as: the best response by a player facing a continuum of adversarial players, the optimal control used to manage a distributed system or also the free energy or rate function of some distributed physical systems. All the associated PDEs are written on a set of measures on a smooth finite dimensional domain and raise new and difficult analytical challenges such as regularity and stability of the solutions, or the existence of weak notions of solutions. A systematic understanding of those equations is missing at the time, but it would lead to: derive rigorously the PDEs form the underlying models, justify numerical computations and, most importantly, to prove quantitative estimates and several properties of the modelled systems, which are otherwise out of reach because of the absence of explicit solutions.
The key challenges targeted in this proposal are concentrated on two PDEs on spaces of measures, which are for the moment only understood in particular regimes, often very simplified compared to their original motivations. The first one is the mean field game (MFG) master equation: we intend to obtain a precise theory of regularity which will then help us both to extend the actual theory to more realistic MFGs and to have a better understanding of the stability of MFG equilibria. The second one is the Hamilton-Jacobi-Bellman equation: we aim to obtain much more general stability properties in order to treat practical mean field optimal control problems and mean field physical systems. Moreover, we also plan to introduce new PDEs modelling the optimal control of MFG master equations, thus raising new mathematical challenges. The design of numerical schemes for such equations will complement the program.

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(opens in new window) ERC-2025-STG

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Host institution

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Net EU contribution

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.

€ 1 438 541,00
Address
RUE MICHEL ANGE 3
75794 PARIS
France

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Region
Ile-de-France Ile-de-France Paris
Activity type
Research Organisations
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Total cost

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.

€ 1 438 541,00

Beneficiaries (1)

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