Work

Marginal Girsanov Reweighting: Stable Variance Reduction for Long-Time-Scale Dynamics from Biased Simulation

Year: 2026

Type: article

Abstract: Recovering unbiased kinetic and thermodynamic observables from enhanced sampling simulations is a central challenge in rare-event sampling. Classical Girsanov Reweighting (GR) offers a principled solution by yielding exact pathwise probability ratios between biased and unbiased processes. However, the variance of GR weights grows rapidly with time, rendering it impractical for long-horizon reweighting. We introduce Marginal Girsanov Reweighting (MGR), which mitigates variance explosion by marginalizing over intermediate paths, producing stable and scalable weights for long-time-scale dynamics. Experiments on various molecular dynamics (MD) systems demonstrate that MGR accurately recovers unbiased kinetic properties from trajectories generated under biased simulations.

Source: Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.6c00777

Authors: Yan Wang, Hao Wu, Simon Olsson

Institutions: Tongji University, Shanghai Jiao Tong University, Chalmers University of Technology, University of Gothenburg

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Knut och Alice Wallenbergs Stiftelse, Vetenskapsrådet

Awards: G1050411572, NAISS