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Data from: Physically interpretable surrogate modeling of thermal fields in electronics cooling using combined proper orthogonal decomposition and neural networks

Curl, Daniel1; Hu, Han1

Published Apr 29, 2026 on Dryad. https://doi.org/10.5061/dryad.k0p2ngfp5

Data files

Apr 29, 2026 version files 1.38 GB

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Abstract

As advances in semiconductor technology drive higher power densities, thermal management is becoming increasingly crucial to maintaining the performance and reliability of devices. In recent decades, computational fluid dynamics (CFD) has been widely used for thermal management system design. However, the computational requirement of CFD limits its use for comprehensive system optimization and digital twin active control systems, which require quick, high-fidelity thermal field prediction. Data-driven surrogate models address the speed limitations of CFD but are often treated as black boxes whose predictions are physically uninterpretable. Neural networks (NN) combined with proper orthogonal decomposition (POD) maintain the speedup of other surrogate models while offering a physically interpretable latent space. The interpretability of POD-NN learned representations remains largely unexplored. This work develops and demonstrates a POD-NN surrogate model framework to predict 2D thermal fields for a liquid-cooled dual-chip cold plate with four retained POD modes. The resultant model predicts fields 412,500 faster than CFD. An error decomposition analysis is used to assess the contribution of POD-based and NN-based error across a range of dataset sizes and identify an error floor dependent on the number of modes retained rather than dataset size. A systematic Pearson correlation analysis is used to link all four POD coefficients to identifiable physical features, and thermal resistance is mapped in latent space. The presented framework enables sensitivity analysis, design optimization, and active thermal control within the physics-aware POD coefficient space, and advances POD-NN architectures from accelerators to interpretable engineering design tools.