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statistical physics

topic1 events
papersSEP 10 04:00 UTC

New Paper Addresses Mode Coverage Gaps in Normalizing Flow Boltzmann Generators

A new arXiv study examines why normalizing flow Boltzmann generators trained with forward KL divergence can miss portions of a target distribution when the available training samples are biased or incomplete. The authors propose using variation in the log-ratio of importance weights as a signal to detect when a flow's samples fail to cover target modes. The approach aims to improve the reliability of flow-based samplers used in statistical physics and molecular simulation.

generative-models/sampling/physics/machine-learning