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

Samuela Rokocakau; Panagiotis Kakosimos; Giulia Tresca; Pericle Zanchetta

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arXiv · 2610.20041

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

Samuela Rokocakau·Panagiotis Kakosimos·Giulia Tresca·Pericle Zanchetta

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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.

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BibTeX RIS

Samuela Rokocakau, Panagiotis Kakosimos, Giulia Tresca, Pericle Zanchetta. 2026-10-14. Sequential Learning-Based Physics-Informed Neural Network Modeling for Parameter Estimation on a Power Converter. https://arxiv.org/abs/2610.20041

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