Physics-Informed Neural Network for Sparse-Sensor Inverse Heat Conduction
DOI:
https://doi.org/10.66131/JDSC13202650-59Keywords:
Physics-informed neural network, nverse heat conduction, Sparse sensor, Thermal field reconstruction, Parameter identification, Sensitivity analysisAbstract
This study develops a sparse-sensor physics-informed neural network (PINN) to reconstruct a transient two-dimensional thermal
field and identify thermal diffusivity, volumetric heat loss, and source amplitude from noisy point measurements. Sixteen interior
sensors provide 5424 observations for a moving-source benchmark. The nominal model achieves a mean root-mean-square error
(RMSE) of 3.52 × 10−3 normalized temperature units and a global relative L2 error of 2.87 × 10−2. The corresponding relative
errors in the three identified coefficients are 5.04%, 24.54%, and 7.90%. Under matched data, architecture, initialization, and
iteration budget, the physics-informed model reduces mean RMSE by 96.03% relative to a conventional data-only neural network
and by 95.56% relative to spatiotemporal interpolation. One-factor-at-a-time tests indicate that spatial sensor scarcity and loss
weighting are more influential than temporal subsampling over the ranges examined. The results show that governing-equation
constraints provide strong regularization for sparse thermal reconstruction; however, the sensitivity results are diagnostic rather
than statistical, and experimental validation remains necessary before deployment
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