mlsanityData to 7 Oct 2026

Computing Committor Functions for the Study of Rare Events Using Deep Learning

arXiv 1906.06285 ↗First posted 14 Jun 2019physics.comp-ph cs.LG cs.NAcited by 29

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Abstract (arXiv)

The committor function is a central object of study in understanding transitions between metastable states in complex systems. However, computing the committor function for realistic systems at low temperatures is a challenging task, due to the curse of dimensionality and the scarcity of transition data. In this paper, we introduce a computational approach that overcomes these issues and achieves good performance on complex benchmark problems with rough energy landscapes. The new approach combines deep learning, data sampling and feature engineering techniques. This establishes an alternative practical method for studying rare transition events between metastable states in complex, high dimensional systems.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jun 2019.

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Citing papers per year: 2021: 5; 2022: 1; 2023: 8; 2024: 6; 2025: 5; 2026: 4.

29 papers.

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  1. 2022Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution Equationscited by 55
  2. 2021Interpolating between BSDEs and PINNs: deep learning for elliptic and parabolic boundary value problemscited by 21
  3. 2023Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networkscited by 18
  4. 2021Reinforcement learning of rare diffusive dynamicscited by 14
  5. 2023Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemescited by 10
  6. 2023Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event predictioncited by 9
  7. 2023A Finite Expression Method for Solving High-Dimensional Committor Problemscited by 9
  8. 2023Diffusion Methods for Generating Transition Pathscited by 8
  9. 2021Supervised Learning and the Finite-Temperature String Method for Computing Committor Functions and Reaction Ratescited by 5
  10. 2024The fast committor machine: Interpretable prediction with kernelscited by 5
  11. 2021Computing the Invariant Distribution of Randomly Perturbed Dynamical Systems Using Deep Learningcited by 4
  12. 2024Deep Learning Method for Computing Committor Functions with Adaptive Samplingcited by 4
17 more
  1. 2024Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensemblescited by 3
  2. 2025Estimating Committor Functions via Deep Adaptive Sampling on Rare Transition Pathscited by 3
  3. 2021Active Learning for Saddle Point Calculationcited by 2
  4. 2023Data-driven path collective variablescited by 2
  5. 2024Accelerated Sampling of Rare Events using a Neural Network Bias Potentialcited by 2
  6. 2024Machine Learning of Slow Collective Variables and Enhanced Sampling via Spatial Techniquescited by 2
  7. 2023Tensorizing flows: a tool for variational inferencecited by 1
  8. 2023Probing reaction channels via reinforcement learningcited by 1
  9. 2025An exact multiple-time-step variational formulation for the committor and the transition ratecited by 1
  10. 2026SCARCE: Scalable Cascade Analysis for Rare-event Characterisation via Embeddingscited by 1
  11. 2024Computing Transition Pathways for the Study of Rare Events Using Deep Reinforcement Learningcited by 0
  12. 2025Accelerated Bayesian Optimal Experimental Design via Conditional Density Estimation and Informative Datacited by 0
  13. 2025Nonparametric Reaction Coordinate Optimization with Histories: A Framework for Rare Event Dynamicscited by 0
  14. 2025FMint-SDE: A Multimodal Foundation Model for Accelerating Numerical Simulation of SDEs via Error Correctioncited by 0
  15. 2026A Priori Sampling of Transition States with Guided Diffusioncited by 0
  16. 2026Reactive Flux Matching: Mechanism Discovery and Adaptive Sampling of Rare Eventscited by 0
  17. 2026Machine learning kinetics from molecular dynamics datacited by 0