Sparse data is fundamental to scientific simulations in biology and physics, from single-cell gene expression to particle calorimetry, where exact zeros encode physical absence rather than weak signal. However, existing diffusion models lack the physical rigor to faithfully represent this sparsity. This work introduces Sparse Data Diffusion (SDD), a generative method that explicitly models exact zeros via Sparsity Bits, unifying efficient ML generation with physically grounded sparsity handling. Empirical validation in particle physics and single-cell biology demonstrates that SDD achieves higher fidelity than baseline methods in capturing sparse patterns critical for scientific analysis, advancing scalable and physically faithful simulation.
Cite
@article{arxiv.2502.02448,
title = {Sparse Data Diffusion for Scientific Simulations in Biology and Physics},
author = {Phil Ostheimer and Mayank Nagda and Andriy Balinskyy and Jean Radig and Carl Herrmann and Stephan Mandt and Marius Kloft and Sophie Fellenz},
journal= {arXiv preprint arXiv:2502.02448},
year = {2026}
}
Comments
This paper won the Best Paper Award at the SimBioChem workshop at EurIPS 2025