ArticleSIAM review. Society for Industrial and Applied Mathematics2018
Trajectory stratification of stochastic dynamics.
Article in SIAM review. Society for Industrial and Applied Mathematics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- BAD-NEUS: Rapidly converging trajectory stratification.The Journal of chemical physics · 2024Article
- Tertiary Plasticity Drives the Efficiency of Enterocin 7B Interactions with Lipid Membranes.The journal of physical chemistry. B · 2024Article
- ScMiles2: A Script to Conduct and Analyze Milestoning Trajectories for Long Time Dynamics.Journal of chemical theory and computation · 2022Article
- Computing transition path theory quantities with trajectory stratification.The Journal of chemical physics · 2022Article
- Long-Time-Scale Predictions from Short-Trajectory Data: A Benchmark Analysis of the Trp-Cage Miniprotein.Journal of chemical theory and computation · 2021Article
- Enhanced Jarzynski free energy calculations using weighted ensemble.The Journal of chemical physics · 2020Article
- ScMile: A Script to Investigate Kinetics with Short Time Molecular Dynamics Trajectories and the Milestoning Theory.Journal of chemical theory and computation · 2020Article
- OPTIMIZING WEIGHTED ENSEMBLE SAMPLING OF STEADY STATES.Multiscale modeling & simulation : a SIAM interdisciplinary journal · 2020Article
- Galerkin approximation of dynamical quantities using trajectory data.The Journal of chemical physics · 2019Article
- Entropic constraints on the steady-state fitness of competing self-replicators.The Journal of chemical physics · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We present a general mathematical framework for trajectory stratification for simulating rare events. Trajectory stratification involves decomposing trajectories of the underlying process into fragments limited to restricted regions of state space (strata), computing averages over the distributions of the trajectory fragments within the strata with minimal communication between them, and combining those averages with appropriate weights to yield averages with respect to the original underlying process. Our framework reveals the full generality and flexibility of trajectory stratification, and it illuminates a common mathematical structure shared by existing algorithms for sampling rare events. We demonstrate the power of the framework by defining strata in terms of both points in time and path-dependent variables for efficiently estimating averages that were not previously tractable.
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Registered trials
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