快速多物理传感器响应预测的福利埃神经运算符:在热,声和流量测量系统中的应用
Ali Sayghe1, Mohammed Mousa1, Salem Batiyah1
1Department of Electrical Engineering, Yanbu Industrial College, Yanbu 46452, Saudi Arabia.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
里叶神经运算器 (FNO) 加速传感器响应预测在热,声和流域. 这种人工智能方法比传统方法提供了显著的加快速度,使实时应用程序和智能仪器的进步成为可能.
科学领域:
- 计算物理和工程计算物理和工程
- 人工智能和机器学习
- 传感器技术和仪器仪表
背景情况:
- 传感器模拟的传统数值方法 (FEM,CFD) 在计算上昂贵,限制了实时应用.
- 准确和快速的传感器响应预测对于数字双胞胎,实时系统和设计优化至关重要.
研究的目的:
- 开发和验证使用福里埃神经运算子 (FNO) 作为多物理传感器响应预测的高效替代模型的新框架.
- 为了证明FNO在热,声和流量测量领域的计算效率和准确性.
主要方法:
- 利用弗里耶神经运算符 (FNO),用于学习无限维函数空间之间的映射,以进行分辨率不变的预测.
- 开发了一种混合H-FNO架构,将光谱运算符与局部卷积层结合起来,以解决光谱偏差.
- 在模拟数据集上训练FNO模型,并根据持久数据和实验结果进行验证.
主要成果:
- 实现了显著的加速度:热 (R2>0.98) 8300×,声 (MAE<0.5 dB) 4000×,流量为31,000× (>97%的准确性).
- 将推断时间从几分钟缩短到几毫秒,证明了显著的计算效率.
- 通过多个案例研究和热传感器实验数据验证了框架的有效性.
结论:
- 基于FNO的替代模型提供了一种强大而高效的解决方案,用于在不同物理领域加速传感器模拟.
- 拟议的框架允许实时传感器校准,不确定性量化和设计优化,支持工业4.0.
- 建立了FNO作为人工智能增强仪器仪表和测量的关键工具,克服了传统数值方法的局限性.
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