{"ID":3050180,"CreatedAt":"2026-06-04T02:13:16.786527022Z","UpdatedAt":"2026-06-06T07:53:07.675991959Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04582","arxiv_id":"2606.04582","title":"Reconstructing Unobservable Temperature Fields via Simulation-Aided Intelligent Sensing","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.","short_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...","url_abs":"https://arxiv.org/abs/2606.04582","url_pdf":"https://arxiv.org/pdf/2606.04582v1","authors":"[\"Monika Stipsitz\",\"Hèlios Sanchis-Alepuz\",\"Jacob Reynvaan\",\"Silvester Sabathiel\"]","published":"2026-06-03T08:16:33Z","proceeding":"physics.comp-ph","tasks":"[\"physics.comp-ph\",\"cs.LG\",\"physics.app-ph\"]","methods":"[]","has_code":false}