ArticleProceedings of the National Academy of Sciences of the United States of America2022
Adaptive Monte Carlo augmented with normalizing flows.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Bridging Control, Inference, Transport, and Thermodynamics: From Theory to Applications in Learning.ArXiv · 2026Article
- Article
- Unveiling hidden intermediate states in protein folding with AI-based conditional transition clustering.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Latent thermodynamic flows: unified representation learning and generative modeling of temperature-dependent behaviors from limited data.Chemical science · 2026Article
- Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data.Journal of chemical theory and computation · 2026Article
- Perspective: An outlook on fluorescence tracking.The Journal of chemical physics · 2025Article
- Flow perturbation to accelerate Boltzmann sampling.Nature communications · 2025Article
- Physics-Inspired Single-Particle Tracking Accelerated with Parallelism.bioRxiv : the preprint server for biology · 2025Article
- Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials.ACS physical chemistry Au · 2025Review
- Deep learning probability flows and entropy production rates in active matter.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Enhanced Sampling with Machine Learning.Annual review of physical chemistry · 2024Review
- Bayesian Mechanistic Inference, Statistical Mechanics, and a New Era for Monte Carlo.Journal of chemical theory and computation · 2024Review
- Free energies at QM accuracy from force fields via multimap targeted estimation.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Review
- A Monte Carlo algorithm to improve the measurement efficiency of low-field nuclear magnetic resonance.Scientific reports · 2023Article
- Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals.Proceedings of machine learning research · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
SignificanceMonte Carlo methods, tools for sampling data from probability distributions, are widely used in the physical sciences, applied mathematics, and Bayesian statistics. Nevertheless, there are many situations in which it is computationally prohibitive to use Monte Carlo due to slow "mixing" between modes of a distribution unless hand-tuned algorithms are used to accelerate the scheme. Machine learning techniques based on generative models offer a compelling alternative to the challenge of designing efficient schemes for a specific system. Here, we formalize Monte Carlo augmented with normalizing flows and show that, with limited prior data and a physically inspired algorithm, we can substantially accelerate sampling with generative models.
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