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Generative modeling of conditional probability distributions on the level-sets of collective variables

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  • Given a probability distribution $\mu$ in $\mathbb{R}^d$ represented by data, we study in this paper the generative modeling of its conditional probability distributions on the level-sets of a collective variable $\xi: \mathbb{R}^d \rightarrow \mathbb{R}^k$, where $1 \le k<d$. We propose a general and efficient learning approach that is able to learn generative models on different level-sets of $\xi$ simultaneously. To improve the learning quality on level-sets in low-probability regions, we also propose a strategy for data enrichment by utilizing data from enhanced sampling techniques. We demonstrate the effectiveness of our proposed learning approach through concrete numerical examples. The proposed approach is potentially useful for the generative modeling of molecular systems in biophysics, for instance.
Metadaten
Author:Fatima-Zahrae Akhyar, Wei Zhang, Gabriel Stoltz, Christof Schütte
Document Type:Article
Year of first publication:2025
ArXiv Id:http://arxiv.org/abs/2512.17374
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
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