Ensemble Visualization With Variational Autoencoder

2025-09-16

AI Summary

Visualizing ensemble data in low dimensions while quantifying associated uncertainties remains challenging. Method: This paper proposes a structured probabilistic representation framework based on variational autoencoders (VAEs). It employs unsupervised learning to map high-dimensional ensemble features into a latent space governed by a standard Gaussian prior, and—uniquely—enables analytical probabilistic modeling of the generative distribution directly in the latent space. This supports exact confidence interval estimation and density analysis. Unlike conventional dimensionality reduction techniques, the framework explicitly models a multivariate Gaussian posterior distribution, thereby structuring and interpreting intrinsic ensemble uncertainty. Results: Experiments on weather forecast ensembles demonstrate substantial improvements in low-dimensional representation fidelity, alongside strong generalizability and scalability. The approach establishes a novel paradigm for uncertainty-aware scientific visualization.

Technology Category

• Reasoning under Uncertainty: Uncertainty Representations
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Representation Learning for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Visualizing data ensembles using probabilistic latent representations
• Transforming spatial features via variational autoencoder unsupervised learning
• Enabling analytical confidence intervals and density estimation

Innovation

• Variational autoencoder for ensemble visualization
• Latent space with Gaussian distribution representation
• Analytical confidence intervals and density estimation