Similar papers
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 str...
Azin Mehrjoo, E. Tronci, Babak Moaveni
· e-Journal of Nondestructive... · 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...
Libin Wen, Jinji Xi, Tannan Xiao et al.
· Energies · 1 citation
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
· Journal of Structural Engine... · 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.
· Buildings · 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
· Proceedings of the Instituti... · 0 citations