A Real-Time, Auto-Regression Method for In-Situ Feature Extraction in Hydrodynamics Simulations
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910911640371200 |
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| author | Yan, Kewei Yan, Yonghong |
| author_facet | Yan, Kewei Yan, Yonghong |
| contents | Hydrodynamics simulations are powerful tools for studying fluid behavior under physical forces, enabling extraction of features that reveal key flow characteristics. Traditional post-analysis methods offer high accuracy but incur significant computational and I/O costs. In contrast, in-situ methods reduce data movement by analyzing data during the simulation, yet often compromise either accuracy or performance. We propose a lightweight auto-regression algorithm for real-time in-situ feature extraction. It applies curve-fitting to temporal and spatial data, reducing data volume and minimizing simulation overhead. The model is trained incrementally using mini-batches, ensuring responsiveness and low computational cost. To facilitate adoption, we provide a flexible library with simple APIs for easy integration into existing workflows. We evaluate the method on simulations of material deformation and white dwarf (WD) mergers, extracting features such as shock propagation and delay-time distribution. Results show high accuracy (94.44%-99.60%) and low performance impact (0.11%-4.95%) demonstrating the method's effectiveness for accurate and efficient in-situ analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10632 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Real-Time, Auto-Regression Method for In-Situ Feature Extraction in Hydrodynamics Simulations Yan, Kewei Yan, Yonghong Distributed, Parallel, and Cluster Computing Hydrodynamics simulations are powerful tools for studying fluid behavior under physical forces, enabling extraction of features that reveal key flow characteristics. Traditional post-analysis methods offer high accuracy but incur significant computational and I/O costs. In contrast, in-situ methods reduce data movement by analyzing data during the simulation, yet often compromise either accuracy or performance. We propose a lightweight auto-regression algorithm for real-time in-situ feature extraction. It applies curve-fitting to temporal and spatial data, reducing data volume and minimizing simulation overhead. The model is trained incrementally using mini-batches, ensuring responsiveness and low computational cost. To facilitate adoption, we provide a flexible library with simple APIs for easy integration into existing workflows. We evaluate the method on simulations of material deformation and white dwarf (WD) mergers, extracting features such as shock propagation and delay-time distribution. Results show high accuracy (94.44%-99.60%) and low performance impact (0.11%-4.95%) demonstrating the method's effectiveness for accurate and efficient in-situ analysis. |
| title | A Real-Time, Auto-Regression Method for In-Situ Feature Extraction in Hydrodynamics Simulations |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.10632 |