Sparse Data Diffusion for Scientific Simulations in Biology and Physics

TL;DR

This paper introduces Sparse Data Diffusion (SDD), a generative model that explicitly handles exact zeros in sparse scientific data, improving fidelity in simulations for biology and physics.

Contribution

The work presents SDD, a novel diffusion approach that models physical sparsity using Sparsity Bits, unifying machine learning generation with physical accuracy.

Findings

SDD outperforms baseline methods in fidelity for sparse data

Empirical validation in physics and biology demonstrates improved pattern capturing

Advances scalable, physically faithful scientific simulations

Abstract

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.