Sequential Learning-Based Physics-Informed Neural Network Modeling for Parameter Estimation on a Power Converter

Abstract:A staged physics-informed neural network (PINN) is proposed for identifying the inductance, capacitance and parasitic resistances of a DC buck converter from time-domain data. Gradients are released in the order of L to C to rL to rC, a sequence derived from sensitivity analysis. The five-stage schedule recovers L and C to within 5% and the series ESR to 10%, while a three-stage variant attains the same accuracy 11% faster. An 84 factorial sweep confirms that the trained PINN remains accurate for simultaneous 8% perturbations of all four components, achieving a waveform RMSE of 2.6 x 10-2 over 5 ms-an order-of-magnitude better than a simultaneous-training baseline.

Machine Learning (cs.LG); Systems and Control (eess.SY)

arXiv:2610.20041 [cs.LG]

(or arXiv:2610.20041v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.20041 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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