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Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

Author

Gajendra Yadav

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Open access Aug 2026

Cycle-Consistent Framework for Mooring Tension Prediction in Floating Offshore Wind Turbines

The accurate prediction of mooring and structural tension is essential for efficient structural health monitoring (SHM) of floating offshore wind turbines (FOWTs), which are exposed to extreme harsh and dynamic sea conditions. This paper presents a data-driven framework inspired by the Deep Operator Network (DeepONet) architecture to directly anticipate time-dependent mooring tension responses from time-varying platform motion. A novel cycle-consistency constraint is integrated into the traditional forward network to improve the model's physical consistency, stability, and robustness. Within this framework, the forward network acquires the mapping from the three translatory platform's motion and temporal inputs to the appropriate tension responses, while an auxiliary inverse network is developed to rebuild the original motion sequences from the predicted tensions. The cycle-consistency loss guarantees aggrement between the original and reconstructed signals, thus regularizing the training process and guiding the network towards physically consistent predictions. This approach is particularly advantageous for sparse or noisy datasets, as conventional data-driven models frequently experience overfitting and provide non-physical predictions. The framework is validated using OpenFAST's numerically simulated FOWT datasets generated under realistic environmental loading conditions. The sensing configuration consists of platform-mounted motion sensors measuring the three translational degrees of freedom (surge, sway, and heave) together with tension sensors installed at the fairlead locations of the mooring lines. Results demonstrate that the proposed cycle-consistent architecture improves prediction accuracy and stability compared to independently trained forward networks, particularly in sparse and noisy data regimes. This framework signifies a significant advancement in physics-informed, data-efficient SHM methods for FOWTs. It also provides a basis for the development of sophisticated digital twins and facilitates real-time monitoring and control of FOWTs.

Kajal Thakur, Nikhil Mahar, Gajendra Yadav et al. · 0 citations
Open access Aug 2026

Structural parameter identification with hybrid physics informed neural network

System identification (SI) is critical for ensuring the reliability of structural and mechanical components across engineering applications. Traditional model-based SI methods often struggle with complex dynamics and the scarcity of accurate physical models, while purely data-driven, model-free approaches though simple and fast lack physical interpretability and suffer from poor generalization. Recent advances such as physics-informed neural networks (PINNs) combine data and physics to overcome these limitations and have shown strong promise for structural health monitoring (SHM). However, most existing methods still require knowledge of the input force, which limits their practical use for parameter estimation. To address this challenge, an input-robust hybrid physics informed neural network (rHPINN) framework is proposed that integrates physics-based system dynamics with the temporal learning capability of HPINN. An output-injection strategy enables rejection of unknown input forces, allowing accurate estimation of system states and spatial health parameters without input force measurements. By explicitly preserving temporal dependencies often overlooked in conventional PINNs the method achieves stable, physics-consistent system identification. Numerical simulations on systems demonstrate that rHPINN remains robust under unknown excitation, measurement noise, sparse data, and varying damage scenarios, highlighting its potential for real-world SHM under uncertain conditions.

Nikhil Mahar, Gajendra Yadav, Kajal Thakur et al. · 0 citations