2025/04/14 by Kewei Yan, Yan, Kewei, Yonghong Yan +1 · 1 citation
Computer Science · Engineering · Physics and Astronomy · #Computer Graphics and Visualization Techniques #Distributed #FOS: Computer and information sciences #Lattice Boltzmann Simulation Studies #Model Reduction and Neural Networks #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2504.10632
openalex publication_date 2025/04/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.