# SENSE: Self-Supervised Neural Embeddings for Spatial Ensembles

arXiv: https://arxiv.org/abs/2512.11145 · HTML: https://mlsanity.com/paper/2512.11145/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Builds on (most cited first, 12 of 12)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters](https://mlsanity.com/paper/2402.00608/) (2024)
- [HyperFLINT: Hypernetwork-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization](https://mlsanity.com/paper/2412.04095/) (2024)
- [GSCache: Real-Time Radiance Caching for Volume Path Tracing using 3D Gaussian Splatting](https://mlsanity.com/paper/2507.19718/) (2025)
- [FLINT: Learning-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization](https://mlsanity.com/paper/2409.19178/) (2024)
- [Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer Models](https://mlsanity.com/paper/2405.14437/) (2024)
- [ENTIRE: Learning-based Volume Rendering Time Prediction](https://mlsanity.com/paper/2501.12119/) (2025)
- [Machine Learning for Scientific Visualization: Ensemble Data Analysis](https://mlsanity.com/paper/2511.23290/) (2025)

## Built on by (most cited first, 1 of 1)

- [TRACE: Reconstruction-Based Anomaly Detection in Ensemble and Time-Dependent Simulations](https://mlsanity.com/paper/2601.08659/) (2026)

## Abstract (arXiv)

Analyzing and visualizing scientific ensemble datasets with high dimensionality and complexity poses significant challenges. Dimensionality reduction techniques and autoencoders are powerful tools for extracting features, but they often struggle with such high-dimensional data. This paper presents an enhanced autoencoder framework that incorporates a clustering loss, based on the soft silhouette score, alongside a contrastive loss to improve the visualization and interpretability of ensemble datasets. First, EfficientNetV2 is used to generate pseudo-labels for the unlabeled portions of the scientific ensemble datasets. By jointly optimizing the reconstruction, clustering, and contrastive objectives, our method encourages similar data points to group together while separating distinct clusters in the latent space. UMAP is subsequently applied to this latent representation to produce 2D projections, which are evaluated using the silhouette score. Multiple types of autoencoders are evaluated and compared based on their ability to extract meaningful features. Experiments on two scientific ensemble datasets - channel structures in soil derived from Markov chain Monte Carlo, and droplet-on-film impact dynamics - show that models incorporating clustering or contrastive loss marginally outperform the baseline approaches.