Physics-Informed Neural Networks for Thermophysical Property Retrieval

• URL: http://arxiv.org/abs/2511.23449v1
• Date: Fri, 28 Nov 2025 18:41:08 GMT
• Title: Physics-Informed Neural Networks for Thermophysical Property Retrieval
• Authors: Ali Waseem, Malcolm Mielle,
• Abstract summary: Inverse heat problems refer to the estimation of material thermophysical properties given observed or known heat diffusion behaviour.<n>Here, we present a PINN-based iterative framework to estimate the thermal conductivity k of a wall from a set of thermographs.
• Score: 0.9668407688201359
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inverse heat problems refer to the estimation of material thermophysical properties given observed or known heat diffusion behaviour. Inverse heat problems have wide-ranging uses, but a critical application lies in quantifying how building facade renovation reduces thermal transmittance, a key determinant of building energy efficiency. However, solving inverse heat problems with non-invasive data collected in situ is error-prone due to environmental variability or deviations from theoretically assumed conditions. Hence, current methods for measuring thermal conductivity are either invasive, require lengthy observation periods, or are sensitive to environmental and experimental conditions. Here, we present a PINN-based iterative framework to estimate the thermal conductivity k of a wall from a set of thermographs; our framework alternates between estimating the forward heat problem with a PINN for a fixed k, and optimizing k by comparing the thermographs and surface temperatures predicted by the PINN, repeating until the estimated k's convergence. Using both environmental data captured by a weather station and data generated from Finite-Volume-Method software simulations, we accurately predict k across different environmental conditions and data collection sampling times, given the temperature profile of the wall at dawn is close to steady state. Although violating the steady-state assumption impacts the accuracy of k's estimation, we show that our proposed framework still only exhibits a maximum MAE of 4.0851. Our work demonstrates the potential of PINN-based methods for reliable estimation of material properties in situ and under realistic conditions, without lengthy measurement campaigns. Given the lack of research on using machine learning, and more specifically on PINNs, for solving in-situ inverse problems, we expect our work to be a starting point for more research on the topic.

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