Structural Dynamic

Structural dynamics research focuses on accurately modeling and predicting the behavior of structures under dynamic loading, aiming to improve design, monitoring, and control. Current efforts center on integrating physics-based models with machine learning techniques, such as physics-informed neural networks (PINNs), Gaussian process regression (GPR), and Bayesian methods, often employing deep learning architectures like LSTMs and convolutional neural networks to handle complex nonlinearities and uncertainties. These advancements are crucial for enhancing structural health monitoring, enabling real-time prediction of structural responses, and accelerating the design process for complex systems, particularly in applications like earthquake engineering and aerospace.