让生成模型精准处理生物物理中的真实零值数据。
Sparse Data Diffusion for Scientific Simulations in Biology and Physics
- 用'稀疏比特'显式建模数据中的真实零值
- 在粒子物理和单细胞生物中生成效果优于基线方法
- 适合需要物理精确性的科学模拟场景
生物与物理科学模拟中普遍存在稀疏数据，如单细胞基因表达或粒子量能器数据，其中零值代表物理上的不存在而非弱信号。现有扩散模型缺乏物理合理性，难以准确表示这种稀疏性。本文提出稀疏数据扩散（SDD），通过引入'稀疏比特'显式建模真实零值，统一高效机器学习生成与物理上合理的稀疏性处理。在粒子物理和单细胞生物学的实证验证表明，SDD在捕捉关键稀疏模式方面比基线方法具有更高保真度，推动了可扩展且物理可信的仿真发展。
原文摘要 · Abstract (English)
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.
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