Reconstructing Unobservable Temperature Fields via Simulation-Aided Intelligent Sensing
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 fi...
| Authors | Monika Stipsitz, Hèlios Sanchis-Alepuz, Jacob Reynvaan et al. |
|---|---|
| Primary category | physics.comp-ph |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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