10260 2025 eng article 0 -- -- -- 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. 2512.17374 in press yes Creative Commons - CC BY - Namensnennung 4.0 International Fatima-Zahrae Akhyar Wei Zhang Wei Zhang Gabriel Stoltz Christof Schütte Numerical Mathematics Schütte, Christof SFB1114-A5 Zhang, Wei Modeling and Simulation of Complex Processes