most citedStatistical-Physics-Informed Neural Networks (Stat-PINNs): A Machine Learning Strategy for Coarse-graining Dissipative Dynamics

3 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

math.PR2026

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…

cond-mat.stat-mech2023★ 3 cited

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…