Parameter Estimation of a Wave Energy Converter Model using Physics Informed Neural Networks

• João Pinheiro L. P. UFRJ
• Rodrigo de S. Luna UFRJ
• Daniel R. Figueiredo UFRJ

DOI: https://doi.org/10.5753/eniac.2025.14260

Resumo

Wave Energy Converters (WECs) play a vital role in the pursuit of sustainable marine energy, offering a promising solution to harness the vast potential of ocean waves to generate energy. However, the accurate identification of physical parameters governing WEC dynamics is a major challenge, especially when direct measurements of the system are limited or noisy. This paper addresses the inverse problem of estimating unknown parameters in a physical model for a WEC system using Physics-Informed Neural Networks (PINNs). The proposed approach incorporates the governing physical equations as soft constraints, giving rise to a physical loss function that is combined with a data loss function and optimized during training. By generating synthetic data that simulates the dynamic response of a WEC, we employ PINNs to infer three critical physical parameters of the model The approach demonstrates high accuracy, particularly when sufficient data is available for training, and remains robust under varying levels of noise. These findings highlight the suitability of PINNs for inverse problems in wave energy systems, supporting their use in system identification and control.

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