Ensemble Visualization With Variational Autoencoder

• URL: http://arxiv.org/abs/2509.13000v1
• Date: Tue, 16 Sep 2025 12:13:15 GMT
• Title: Ensemble Visualization With Variational Autoencoder
• Authors: Cenyang Wu, Qinhan Yu, Liang Zhou,
• Abstract summary: We present a new method to visualize data ensembles by constructing structured probabilistic representations in latent spaces.<n>Preliminary results on a weather forecasting ensemble demonstrate the effectiveness and versatility of our method.
• Score: 4.539358010738346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new method to visualize data ensembles by constructing structured probabilistic representations in latent spaces, i.e., lower-dimensional representations of spatial data features. Our approach transforms the spatial features of an ensemble into a latent space through feature space conversion and unsupervised learning using a variational autoencoder (VAE). The resulting latent spaces follow multivariate standard Gaussian distributions, enabling analytical computation of confidence intervals and density estimation of the probabilistic distribution that generates the data ensemble. Preliminary results on a weather forecasting ensemble demonstrate the effectiveness and versatility of our method.

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