Reconstructing Unobservable Temperature Fields via Simulation-Aided Intelligent Sensing
2026-06-03 • Machine Learning
Machine Learning
AI summaryⓘ
The authors created a new way to make fake data using physics simulations to help teach a computer how to guess temperatures inside machines. They used this fake data to train a neural network that can figure out the temperature inside stuff just by looking at a few sensors on the outside. Their method works better and faster than older methods, making it useful for watching temperatures in real-time, even where it’s hard to place many sensors.
machine learningneural networktemperature monitoringphysics-based simulationreal-time inferencethermal field reconstructionsparse sensorsindustrial applicationsKriging
Authors
Monika Stipsitz, Hèlios Sanchis-Alepuz, Jacob Reynvaan, Silvester Sabathiel
Abstract
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.