# Spectral estimation from simulations via sketching

arXiv: https://arxiv.org/abs/2007.11026 · HTML: https://mlsanity.com/paper/2007.11026/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

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

- [In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based Regularization](https://mlsanity.com/paper/2511.02659/) (2025)

## Abstract (arXiv)

Sketching is a stochastic dimension reduction method that preserves geometric structures of data and has applications in high-dimensional regression, low rank approximation and graph sparsification. In this work, we show that sketching can be used to compress simulation data and still accurately estimate time autocorrelation and power spectral density. For a given compression ratio, the accuracy is much higher than using previously known methods. In addition to providing theoretical guarantees, we apply sketching to a molecular dynamics simulation of methanol and find that the estimate of spectral density is 90% accurate using only 10% of the data.