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

A deep surrogate modeling approach for distributed control of aggregated air-conditioning loads

A three-layer “Aggregator-Edge-End user” architecture leveraging a data-driven workflow and a hybrid deep learning model combining a Temporal Convolutional Network (TCN) and a Bidirectional Long Short-Term Memory (BiLSTM) network is introduced.

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.

A Dual-Track Feature-Enhanced Physics-Informed Model for Accurate Wind Power Forecasting with Physical Consistency

A hybrid FCM-WGM-BiLSTM-Transformer (FW-BTP) framework integrating Fuzzy C-Means clustering, Weighted Grey Model (WGM) trend extraction, and a coupled BiLSTM-Transformer module is proposed, supporting refined scheduling in modern power systems.

A physics-constrained temporal modeling framework for robust short-term wind power forecasting

Accurate short-term wind power prediction plays a critical role in ensuring stable grid operation, effective energy management, and the large-scale integration of renewable energy systems under highly variable wind conditions. Although data-driven and deep learning models have demonstrated promising forecasting capability, many existing approaches suffer from performance degradation during rapid wind fluctuations due to the lack of embedded physical constraints and the high computational complexity associated with recurrent architectures. To address these limitations, this article proposes a Physics-Guided Residual Temporal Convolutional Network (PG-ResTCN) for short-term wind power forecasting. The proposed framework integrates dilated temporal convolutional learning with residual connections to effectively capture multiscale temporal dependencies in wind power time series while avoiding the sequential computation of recurrent neural networks, thereby improving computational efficiency. Furthermore, a physics-based smoothness constraint is incorporated into the training loss function to enforce physically consistent power ramping behavior that reflects the inherent operational dynamics and inertia of wind turbines, reducing unrealistic fluctuations in predicted power outputs. The effectiveness of the proposed model is validated using a large real-world dataset containing approximately 140,161 hourly samples collected from five wind farm locations, including meteorological variables and corresponding turbine power outputs. Comprehensive experiments are conducted by comparing the proposed method with widely used benchmark models, including Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory networks. Results demonstrate that the proposed PG-ResTCN model achieves superior forecasting performance, obtaining a root mean square error of 0.0359, mean absolute error of 0.0296, and R 2 of 0.9759, outperforming all baseline models. The integration of physics-guided constraints with the temporal convolutional architecture significantly enhances prediction accuracy, stability, and generalization capability. In addition, the proposed framework maintains high computational efficiency and robustness, making it suitable for real-time wind energy forecasting applications, intelligent energy management systems, and microgrid power system operations.

Deep learning empowered stochastic dynamics analysis of single-tower cable-stayed bridge with nonlinear tuned inerter dampers

Hybrid Physics-Data-Driven Framework for Blind Acoustic Impedance Inversion

Conventional acoustic impedance inversion methods have long faced technical bottlenecks such as inaccurate wavelet estimation and strong dependence on initial models. Although existing deep learning approaches can partially alleviate these problems, they often compromise model simplicity and training efficiency, while introducing new challenges such as limited generalizability and heavy reliance on labeled data. To overcome these limitations, this study proposes a lightweight inversion framework that tightly integrates physics-driven and data-driven paradigms. The physics-driven component adopts a neural network architecture largely consistent with traditional modeling processes, enabling direct optimization of physically meaningful parameters through backpropagation, thereby avoiding the construction of excessively complex inverse operators. Meanwhile, regularization methods are introduced to enforce geological prior knowledge (that is, the “layered geological model” assumption) on the network parameters, improving the spatial continuity of the reconstructed impedance models. The data-driven component employs an enhanced 2D U-Net integrated with Class Activation Mapping (U-Net-CAM) to generate accurate reference models from sparse well-log data. Tests on both synthetic and field datasets demonstrate the advantages of the proposed method: (1) physically interpretable network design; (2) strong robustness to noise and reduced dependence on training data; (3) higher accuracy and better spatial continuity compared to conventional and purely data-driven methods. This work provides a new perspective for addressing long-standing challenges in seismic impedance inversion.