Non-equilibrium Statistical Mechanics, Interacting Particle Systems and Fluctuating Hydrodynamics
Main fields of application
Many macroscopic laws arise from large systems of randomly interacting particles far from equilibrium. We study how microscopic interactions produce deterministic hydrodynamic equations and stochastic fluctuation fields.
The programme combines mean-field and hydrodynamic limits with central-limit corrections, large deviations and entropy methods. Conservative SPDEs provide continuum descriptions of fluctuations, while degenerate diffusion and singular interactions create analytical challenges at every scale. We seek quantitative links between particles and fields, including universality of fluctuations and the role of gradient-flow structures. Applications include exclusion and zero-range processes, vortex and aggregation models, and large stochastic systems related to learning.
Current directions
- Mean-field and hydrodynamic limits
- Gaussian fluctuations and large deviations
- Conservative SPDEs
- Entropy and gradient-flow methods
- Singular and degenerate interactions
Related people
Related projects
Selected publications
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The Advective Fisher-Rao Geometry of Deterministic Measure Transport
A novel advective Fisher-Rao metric is introduced for optimization tasks on paths of probability measures governed by the continuity equation. This metric is shown to lead to optimal descent directions. It is then shown that this metric arises naturally from three different…
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Effective fluctuating continuum models for stochastic gradient descent
Workshop report · Oberwolfach Reports 23(1), 821–822 (2026). Fluctuating continuum descriptions of stochastic gradient descent.
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Introduce thermal noise before discretizing, not afterwards!
Workshop report · Oberwolfach Reports 23(1), 803–806 (2026). Incorporating thermal noise before discretising a continuum model.
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Asymptotics of Multi-Scale McKean–Vlasov Diffusions with Super-Linear Kernels: a Lifted Semigroup Approach
In this work, we establish the small-noise asymptotic behaviour (namely, the functional law of large numbers and the large deviation principle) for multi-scale McKean–Vlasov diffusions with super-linear kernels. In this setting, the interaction depends on the laws of both the slow component…
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The Incompressible Navier–Stokes–Fourier System with Thermal Noise
We establish a solution theory for the incompressible Navier–Stokes–Fourier system with thermal noise, posed on the three-dimensional torus. While in the incompressible deterministic setting the equation for the velocity can be solved independently of the temperature, the inclusion of the effects of…
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The Porous Medium Equation: Multiscale Integrability in Large Deviations
We consider a zero-range process $η^N_t(x)$ with superlinear local jump rate, which in a hydrodynamic-small particle rescaling converges to the porous medium equation $\partial_t u=\frac12Δu^α, α>1$. As a main result we obtain a large deviation principle in any scaling regime of vanishing…
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THINNs: Thermodynamically Informed Neural Networks
Physics-Informed Neural Networks (PINNs) are a class of deep learning models aiming to approximate solutions of PDEs by training neural networks to minimize the residual of the equation. Focusing on non-equilibrium fluctuating systems, we propose a physically informed choice of penalization that…
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Convergence rate for Fluctuations of mean field interacting diffusion and application to 2D viscous Vortex model and Coulomb potential
For a system of mean field interacting diffusion on $\mathbb{T}^d$, the empirical measure $μ^N$ converges to the solution $μ$ of the Fokker-Planck equation. Refining this mean field limit as a Central Limit Theorem, the fluctuation process $ρ^N_t= \sqrt{N}( μ^N_t -μ_t)$ convergences to…