The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: AI4S: 6th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications
Authors: Kewei Yan and Yonghong Yan (University of North Carolina at Charlotte)
Abstract: Effective in-situ analysis of target variables in scientific simulations is often constrained by its tight coupling with simulation timesteps, which can degrade performance and limit adaptive control of analysis quality. This work presents a surrogate modeling approach that decouples data collection and analysis from simulation execution. The surrogate models of targeted variables are trained online using early-stage simulation data, and once well-trained, replace the simulation for targeted in-situ analysis, enabling asynchronous analysis and early termination decisions without pretraining or manual tuning. We evaluate it on various applications for different data analysis tasks. Considering training processes during early stages of the simulation, we still achieve speed-ups of 1.20x–3.51x compared to traditional in-situ tracking. At meantime, we keep accuracies of 83.33%–99.60% comparing with original simulation.
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