论文概要
研究领域: ML 作者: Jiaming Li 发布时间: 2026-08-17 arXiv: 2608.16873
中文摘要
高保真有限元模拟可为侧支谐振器提供准确的数值预测，但大规模模拟数据集生成成本高昂，且在模拟标记数据稀缺时纯数据驱动的替代模型可能变得不可靠。本研究开发了一个解析先验学习框架，重用低成本的解析模型来提高有限高保真模拟预算下的数据效率。考虑了两条互补路径：当解析模型在推理时仍可用，将其保留为显式基线，仅用模拟数据学习解析到模拟的差异；当需要自包含的预测器时，先从丰富的低成本评估中蒸馏解析映射为学习先验，再用有限模拟数据校准。该框架在矩形侧支亥姆霍兹谐振器上评估，使用86个模拟标记几何和8998个非重叠的仅解析几何。结果显示，解析先验信息在模拟数据稀缺时能显著改善高保真预测，显式校正和先验蒸馏服务于互补的部署需求。
原文摘要
High-fidelity finite-element simulations can provide accurate numerical predictions for side-branch resonators, but large simulation datasets are expensive to generate and purely data-driven surrogates may become unreliable when simulation-labelled data are scarce. This study develops an analytical-prior learning framework that reuses a low-cost analytical model to improve data efficiency under limited high-fidelity simulation budgets. Two complementary routes are considered. When the analytical model remains available at inference, it is retained as an explicit baseline and the simulation data are used to learn only the analytical-to-simulation discrepancy. When a self-contained predictor is required, the analytical mapping is first distilled from abundant low-cost evaluations into a lear…
— 自动采集于 2026-08-19
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