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This is a Quantitative Study study.

October 3, 2025Open Access

Flow Matching at Scale: A Machine Learning Framework for Efficient Large-Size Sampling of Many-Body Systems

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This framework enhances flow matching to improve sampling in many-body systems, suggesting a method to overcome MCMC limitations.

The flow matching framework generates reliable samples for larger systems without retraining, indicating efficiency.
It shows strong agreement with thermodynamic observables in the 2D XY model, capturing key transitions effectively.
The scale-invariant nature of the learned mappings allows for efficient studies of critical phenomena in various systems.
This method facilitates high-precision studies in thermodynamic limits, hinting at wide applicability in classical and quantum systems.

Lee et al. (2025) studied this question.

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