Sparse Data Diffusion for Scientific Simulations in Biology and Physics
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915745004257280 |
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| author | Ostheimer, Phil Nagda, Mayank Balinskyy, Andriy Radig, Jean Herrmann, Carl Mandt, Stephan Kloft, Marius Fellenz, Sophie |
| author_facet | Ostheimer, Phil Nagda, Mayank Balinskyy, Andriy Radig, Jean Herrmann, Carl Mandt, Stephan Kloft, Marius Fellenz, Sophie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02448 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sparse Data Diffusion for Scientific Simulations in Biology and Physics Ostheimer, Phil Nagda, Mayank Balinskyy, Andriy Radig, Jean Herrmann, Carl Mandt, Stephan Kloft, Marius Fellenz, Sophie Machine Learning 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. |
| title | Sparse Data Diffusion for Scientific Simulations in Biology and Physics |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.02448 |