用仿真生成数据训练神经网络，实现硬件内部温度场的实时重建。
Reconstructing Unobservable Temperature Fields via Simulation-Aided Intelligent Sensing
- 通过随机物理仿真生成工业级温度数据集
- 仅用合成数据训练的神经网络实现高精度温度场重建
- 适合需要实时监测隐藏热分布的工程场景
由于传感器布设位置受限，许多系统中对部件及子结构内部温度分布的实时监测极具挑战。尽管机器学习在众多领域表现优异，但其在高分辨率热监测中的应用受限于高质量训练数据的缺乏。本文提出一种基于随机物理仿真的新型工业数据集生成方法。我们在一个概念验证硬件平台上展示了该方法：仅使用此类合成数据训练的神经网络，即可从嵌入式稀疏传感器中重构内部温度场。基于神经网络的重建不仅比克里金插值更鲁棒，还支持实时推理，使原本不可观测的热态在线监测成为可能。
原文摘要 · Abstract (English)
Real-time monitoring of the temperature distribution within components and sub-structures is a challenging topic in many systems due to restrictions on feasible sensor locations. While machine learning (ML) proves a versatile tool in many applications, its adoption for high-resolution thermal monitoring is hindered by the availability of high-quality datasets for training. In this work, we propose a novel approach for generating datasets for industrial applications based on randomized physics-based simulations. We demonstrate the approach in a proof-of-concept hardware setup: A neural network (NN) trained only on such a synthetic dataset, is used to reconstruct the internal temperature field from sparse sensors embedded in the hardware. The NN-based reconstructions do not only outperform Kriging in robustness but also enable real-time inference, making the method suitable for online monitoring of otherwise unobservable thermal states.
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