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In this note we establish hypocoercivity and exponential relaxation to the Maxwellian for a class of kinetic Fokker-Planck-Alignment equations arising in the studies of collective behavior. Unlike previously known results in this direction…

Analysis of PDEs · Mathematics 2021-07-23 Roman Shvydkoy

We generalize the concept of non-reversible lifts for reversible diffusion processes initiated by Eberle and Lorler (2024) to quantum Markov dynamics. The lifting operation, which naturally results in hypocoercive processes, can be formally…

Probability · Mathematics 2025-05-20 Bowen Li , Jianfeng Lu

This paper is concerned with a modified entropy method to establish the large-time convergence towards the (unique) steady state, for kinetic Fokker-Planck equations with non-quadratic confinement potentials in whole space. We extend…

Analysis of PDEs · Mathematics 2024-01-23 Anton Arnold , Gayrat Toshpulatov

Explicit, momentum-based dynamics for optimizing functions defined on Lie groups was recently constructed, based on techniques such as variational optimization and left trivialization. We appropriately add tractable noise to the…

Statistics Theory · Mathematics 2024-06-19 Lingkai Kong , Molei Tao

An integral relation is derived from the Fokker-Planck equation which connects the steady-state probability currents with the dynamics of relaxation on short timescales in the limit of small perturbation fields. As a consequence of this…

Statistical Mechanics · Physics 2024-03-26 Andrea Auconi

This paper is motivated by the problem of quantitatively bounding the convergence of adaptive control methods for stochastic systems to a stationary distribution. Such bounds are useful for analyzing statistics of trajectories and…

Optimization and Control · Mathematics 2021-10-19 Tyler Lekang , Andrew Lamperski

We study the treatment of the constraints in stochastic quantization method. We improve the treatment of the stochastic consistency condition proposed by Namiki et al. by suitably taking account of the Ito calculus. Then we obtain an…

High Energy Physics - Theory · Physics 2009-10-22 K. Ikegami , T. Kimura , R. Mochizuki

The usual Langevin approach to describe systems driven by noise fails to describe the long time behavior of systems with multiple attractors. The solution of the associated linear Fokker-Planck equation is always unique, even though it…

Statistical Mechanics · Physics 2017-06-20 M. Morillo , J. M. Casado

We develop a generalized hyperdynamics method, which is able to simulate slow dynamics in atomistic general (both energy and entropy-dominated) systems. We show that a few functionals of the pair correlation function, involving two-body…

Statistical Mechanics · Physics 2009-11-11 Xin Zhou , Yi Jiang , Kurt Kremer , Hans Ziock , Steen Rasmussen

We show that the mean-field underdamped Langevin process (associated to the non-linear Vlasov-Fokker-Planck equation) achieves a Nesterov acceleration with respect to the Wasserstein gradient flow of a displacement-convex free energy, in…

Analysis of PDEs · Mathematics 2026-05-22 Pierre Monmarché

We study linear closure relations for the moments' method applied to simple kinetic equations. The equations are linear collisional models (velocity jump processes) which are well suited to this type of approximation. In this simplified, 1…

Analysis of PDEs · Mathematics 2012-06-22 Yves Bourgault , Damien Broizat , Pierre-Emmanuel Jabin

We study the convergence to equilibrium of the mean field PDE associated with the derivative-free methodologies for solving inverse problems. We show stability estimates in the euclidean Wasserstein distance for the mean field PDE by using…

Analysis of PDEs · Mathematics 2019-10-24 J. A. Carrillo , U. Vaes

We investigate whether the stationary solution of the Fokker-Planck equation of the complex Langevin algorithm reproduces the correct expectation values. When the complex Langevin algorithm for an action $S(x)$ is convergent, it produces an…

High Energy Physics - Lattice · Physics 2016-12-21 L. L. Salcedo

In this paper, we study the problem of sampling from log-concave distributions supported on convex, compact sets, with a particular focus on the randomized midpoint discretization of both vanilla and kinetic Langevin diffusions in this…

Machine Learning · Statistics 2025-05-27 Yifeng Yu , Lu Yu

In this paper, we consider Langevin processes with mechanical constraints. The latter are a fundamental tool in molecular dynamics simulation for sampling purposes and for the computation of free energy differences. The results of this…

Statistical Mechanics · Physics 2011-04-19 Tony Lelievre , Mathias Rousset , Gabriel Stoltz

In this work we analyze the inverse problem of recovering the space-dependent potential coefficient in an elliptic / parabolic problem from distributed observation. We establish novel (weighted) conditional stability estimates under very…

Numerical Analysis · Mathematics 2022-12-21 Bangti Jin , Xiliang Lu , Qimeng Quan , Zhi Zhou

This is a review based on the presentation done at the seminar Laurent Schwartz in December 2021. It is announcing results in the forthcoming [Menegaki-Mouhot-Marahrens'22]. This work presents a new simple quantitative method for proving…

Probability · Mathematics 2022-12-02 Angeliki Menegaki , Clément Mouhot

We propose fractional Fokker-Planck equation for the kinetic description of relaxation and superdiffusion processes in constant magnetic and random electric fields. We assume that the random electric field acting on a test charged particle…

Plasma Physics · Physics 2009-11-07 A. Chechkin , V. Gonchar , M. Szydlowski

We study the problem of sampling from a probability distribution on $\mathbb R^p$ defined via a convex and smooth potential function. We consider a continuous-time diffusion-type process, termed Penalized Langevin dynamics (PLD), the drift…

Statistics Theory · Mathematics 2020-06-26 Avetik Karagulyan , Arnak S. Dalalyan

Latent-variable energy-based models (LVEBMs) assign a single normalized energy to joint pairs of observed data and latent variables, offering expressive generative modeling while capturing hidden structure. We recast maximum-likelihood…

Machine Learning · Computer Science 2025-10-20 Shiqin Tang , Shuxin Zhuang , Rong Feng , Runsheng Yu , Hongzong Li , Youzhi Zhang