ArticleJournal of computational physics2023
Predicting rare events using neural networks and short-trajectory data.
Article in Journal of computational physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- Hierarchical geometric deep learning enables scalable analysis of molecular dynamics.The Journal of chemical physics · 2026Article
- A continuous-space analytical framework for committor functions from molecular dynamics.The Journal of chemical physics · 2026Article
- An Exact Multiple-Time-Step Variational Formulation for the Committor and the Transition Rate.The journal of physical chemistry. B · 2026Article
- Building multiscale Markov state models by systematic mapping of temporal communities.Bioinformatics (Oxford, England) · 2026Article
- Iterative variational learning of committor-consistent transition pathways using artificial neural networks.Nature computational science · 2025Article
- Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics.The Journal of chemical physics · 2025Article
- Article
- BAD-NEUS: Rapidly converging trajectory stratification.The Journal of chemical physics · 2024Article
- Accurate estimates of dynamical statistics using memory.The Journal of chemical physics · 2024Article
- Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction.The Journal of chemical physics · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Estimating the likelihood, timing, and nature of events is a major goal of modeling stochastic dynamical systems. When the event is rare in comparison with the timescales of simulation and/or measurement needed to resolve the elemental dynamics, accurate prediction from direct observations becomes challenging. In such cases a more effective approach is to cast statistics of interest as solutions to Feynman-Kac equations (partial differential equations). Here, we develop an approach to solve Feynman-Kac equations by training neural networks on short-trajectory data. Our approach is based on a Markov approximation but otherwise avoids assumptions about the underlying model and dynamics. This makes it applicable to treating complex computational models and observational data. We illustrate the advantages of our method using a low-dimensional model that facilitates visualization, and this analysis motivates an adaptive sampling strategy that allows on-the-fly identification of and addition of data to regions important for predicting the statistics of interest. Finally, we demonstrate that we can compute accurate statistics for a 75-dimensional model of sudden stratospheric warming. This system provides a stringent test bed for our method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.