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Open access Aug 2026

Physics-Informed Neural Network Framework for Input Load Estimation and Virtual Sensing of Offshore Wind Turbines

Recent advances in Physics-Informed Neural Networks (PINNs) have opened new possibilities for integrating structural dynamics and data-driven learning in Structural Health Monitoring (SHM). This work presents a physics-informed framework for input load estimation and virtual sensing of offshore wind turbine support structures, where the governing dynamics of the system are embedded directly into the learning process. Unlike purely data-driven models that require extensive labeled datasets, the proposed approach leverages known physical relationships among displacement, velocity, acceleration, and external loads to enhance interpretability and generalization. The method adopts an encoder–decoder neural architecture that maps measured accelerations and strains to a reduced-order modal space before decoding the corresponding dynamic responses and reconstructing the applied loads through embedded structural dynamics relationships. Physical consistency is enforced through the equations of motion and differential constraints between displacement, velocity, and acceleration, while automatic differentiation ensures temporal consistency without requiring explicit load data during training. This hybrid approach captures the temporal and spatial evolution of loads even with limited or noisy measurements. The framework is first validated on numerical simulations of an offshore wind turbine, accurately recovering unmeasured input loads and structural responses across diverse operating conditions. It is then demonstrated using experimental vibration data, confirming its robustness to sensor noise and sparse instrumentation. Results show that the proposed physics-informed strategy can recover complex loading patterns and provide virtual measurements that are otherwise inaccessible in practice. Overall, study advances the use of PINNs for inverse input load estimation problem in SHM, offering a computationally efficient and generalizable tool for condition monitoring and fatigue assessment of large-scale energy infrastructure.

Azin Mehrjoo, E. Tronci, Babak Moaveni · 0 citations
Open access Jun 2026

A Physics-Informed Deep Learning Method for Wind Turbine Impedance Modeling

A gray-box framework—the Physics-Informed Hybrid Model (PIHM)—that integrates a simplified physical impedance branch with a Bidirectional Long Short-Term Memory (Bi-LSTM) network in a novel parallel architecture is proposed, establishing the PIHM as a reliable, parameter-free tool for impedance-based stability analysis of modern wind power systems.

Libin Wen, Jinji Xi, Tannan Xiao et al. · 1 citation
#reinforcement learning Open access Oct 2026

Seismic Control of a Smart Base-Isolated Building with Nonlinear Behavior Using Deep Reinforcement Learning

DRL is highlighted as a promising data-driven strategy for robust and adaptive control of nonlinear structural systems under partial observability by addressing a critical limitation of passive systems and accelerates the decay of residual vibrations.

Takehiko Asai · 0 citations
Open access Aug 2026

A Unified Physics-Constrained Deep Reinforcement Learning Framework for Parameter Identification of Nonlinear Hysteretic Models

This study develops a unified physics-constrained deep reinforcement learning framework for OpenSees Steel02 and DowelType identification, giving accuracy comparable with tuned PSO at the same online OpenSees-call budget while retaining a reusable learned initialization step.

Hanlin Dong, Chunhua Liu, Mingji Fang et al. · 0 citations
Preprint Jul 2026

SpectONet: A Physics-Guided Spectral Deep Operator Network for Euler-Bernoulli Beam Dynamics

SpectONet provides an accurate, computationally efficient, and physically consistent operator-learning framework for structural vibration analysis and achieves at least 64% improvement over the considered baseline models across the three synthetic problems and at least 37% for the real-world problems.

Shivani Saini, R. Vats, A. Sahoo · 0 citations
Aug 2026

Efficient pitching moment prediction for canard-controlled missiles via transfer learning-based deep learning

This work proposes an efficient multilayer neural network framework to predict the pitching moment coefficient of canard-controlled missiles, significantly reducing the need for costly CFD data and providing a powerful surrogate tool for rapid design optimization based on trim angle of attack and geometric parameters.

M. Shojaeefard, Masoud Nobakhti · 0 citations