# Reconstructing Unobservable Temperature Fields via Simulation-Aided Intelligent Sensing

arXiv: https://arxiv.org/abs/2606.04582 · HTML: https://mlsanity.com/paper/2606.04582/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data](https://mlsanity.com/paper/2109.12482/) (2021)
- [Towards Real Time Thermal Simulations for Design Optimization using Graph Neural Networks](https://mlsanity.com/paper/2209.13348/) (2022)
- [Approximating the full-field temperature evolution in 3D electronic systems from randomized "Minecraft" systems](https://mlsanity.com/paper/2209.10369/) (2022)

## Abstract (arXiv)

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.