# Scientific Data Compression and Super-Resolution Sampling

arXiv: https://arxiv.org/abs/2511.13675 · HTML: https://mlsanity.com/paper/2511.13675/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 50 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 17 of 17)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [A Theory of Generative ConvNet](https://mlsanity.com/paper/1602.03264/) (2016)
- [Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model](https://mlsanity.com/paper/1904.09770/) (2019)
- [On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models](https://mlsanity.com/paper/1903.12370/) (2019)
- [Medical image denoising using convolutional denoising autoencoders](https://mlsanity.com/paper/1608.04667/) (2016)
- [Statistical Efficiency of Score Matching: The View from Isoperimetry](https://mlsanity.com/paper/2210.00726/) (2022)
- [Efficiently learning Ising models on arbitrary graphs](https://mlsanity.com/paper/1411.6156/) (2014)
- [Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models](https://mlsanity.com/paper/1605.07252/) (2016)
- [Efficient Learning of Discrete Graphical Models](https://mlsanity.com/paper/1902.00600/) (2019)
- [Provable benefits of score matching](https://mlsanity.com/paper/2306.01993/) (2023)
- [Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization](https://mlsanity.com/paper/2310.01762/) (2023)
- [Efficiently learning and sampling multimodal distributions with data-based initialization](https://mlsanity.com/paper/2411.09117/) (2024)
- [Target-Embedding Autoencoders for Supervised Representation Learning](https://mlsanity.com/paper/2001.08345/) (2020)
- [Programmable Quantum Annealers as Noisy Gibbs Samplers](https://mlsanity.com/paper/2012.08827/) (2020)
- [BUDD: Multi-modal Bayesian Updating Deforestation Detections](https://mlsanity.com/paper/2001.10661/) (2020)
- [Semi-supervised classification using a supervised autoencoder for biomedical applications](https://mlsanity.com/paper/2208.10315/) (2022)

## Abstract (arXiv)

Modern scientific simulations, observations, and large-scale experiments generate data at volumes that often exceed the limits of storage, processing, and analysis. This challenge drives the development of data reduction methods that efficiently manage massive datasets while preserving essential physical features and quantities of interest. In many scientific workflows, it is also crucial to enable data recovery from compressed representations - a task known as super-resolution - with guarantees on the preservation of key physical characteristics. A notable example is checkpointing and restarting, which is essential for long-running simulations to recover from failures, resume after interruptions, or examine intermediate results. In this work, we introduce a novel framework for scientific data compression and super-resolution, grounded in recent advances in learning exponential families. Our method preserves and quantifies uncertainty in physical quantities of interest and supports flexible trade-offs between compression ratio and reconstruction fidelity.