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Temporal Extrapolation Generalization of Proper Orthogonal Decomposition (POD) and Radial Basis Function (RBF)
Wenjun Zhao1,2, Bo Zhang1
1School of Energy and Power Engineering, Dalian University of Technology, Dalian 116024, China.
None:
Efficient and accurate prediction of transient temperature fields is critical for thermal management of electronic devices with multiple heat sources. In this study, a reduced-order surrogate modeling approach is developed based on proper orthogonal decomposition (POD) and radial basis function (RBF) neural networks. The method maps time-conditioned modal coefficients in a parameter-time space, enabling robust temporal extrapolation beyond the training horizon. A multi-heat-source conduction model typical of electronic packages is used as the application scenario. Numerical experiments demonstrate that the proposed POD-RBF surrogate achieves high predictive accuracy (global MRE < 3%) with significantly reduced computational cost, offering strong potential for real-time thermal monitoring and management in electronic systems.
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