59 citations · 128 across the 17 of their papers we have counts for
21 papers
Quantitative Error Estimates for Learning Macroscopic Mobilities from Microscopic Fluctuations
Nicolas Dirr, Zhengyan Wu, Johannes Zimmer
We develop quantitative error estimates connecting microscopic fluctuation of interacting particle systems with the mobilities of their hydrodynamic limits. Focusing on the Symmetr…
Reversibility, covariance and coarse-graining for Langevin dynamics: On the choice of multiplicative noise
Mario Ayala, Nicolas Dirr, Grigorios A. Pavliotis +1
We study the interplay between reversibility, geometry, and the choice of multiplicative noise (in particular Itô, Stratonovich, Klimontovich) in stochastic differential equations…
Horizontal mean curvature flow as a scaling limit of a mean field equation in the Heisenberg group
Giovanna Citti, Nicolas Dirr, Federica Dragoni +1
We derive curvature flows in the Heisenberg group by formal asymptotic expansion of a nonlocal mean-field equation under the anisotropic rescaling of the Heisenberg group. This is…
Statistical-Physics-Informed Neural Networks (Stat-PINNs): A Machine Learning Strategy for Coarse-graining Dissipative Dynamics
Shenglin Huang, Zequn He, Nicolas Dirr +2
Machine learning, with its remarkable ability for retrieving information and identifying patterns from data, has emerged as a powerful tool for discovering governing equations. It…
Conservative stochastic PDE and fluctuations of the symmetric simple exclusion process
Nicolas Dirr, Benjamin Fehrman, Benjamin Gess
In this paper, we provide a continuum model for the fluctuations of the symmetric simple exclusion process about its hydrodynamic limit. The model is based on an approximating sequ…
Asymptotics for optimal controls for horizontal mean curvature flow
Nicolas Dirr, Federica Dragoni, Raffaele Grande
The solutions to surface evolution problems like mean curvature flow can be expressed as value functions of suitable stochastic control problems, obtained as limit of a family of r…