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New PINN method accurately estimates DC buck converter parameters

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a staged physics-informed neural network (PINN) to accurately estimate parameters for a DC buck converter. This method breaks down the parameter identification into sequential stages, starting with inductance (L) and capacitance (C), then moving to parasitic resistances (rL and rC). The staged approach significantly improves accuracy and speed compared to simultaneous training methods, achieving high precision even with simultaneous perturbations of all four components. AI

IMPACT This research could lead to more efficient and accurate modeling of power electronic components, potentially improving their design and performance.

RANK_REASON The item is an academic paper detailing a new modeling approach for parameter estimation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New PINN method accurately estimates DC buck converter parameters

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The item is an academic paper detailing a new modeling approach for parameter estimation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Samuela Rokocakau, Panagiotis Kakosimos, Giulia Tresca, Pericle Zanchetta ·

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

    arXiv:2610.20041v1 Announce Type: new 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 r…

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