A physics-informed neural network (PINN) is developed for modeling time-harmonic Lamb-wave excitation in a two-dimensional elastic waveguide under surface loading. The displacement and stress fields are represented by the network, and its trainable weights are determined by enforcing the first-order elastodynamic system, the traction boundary conditions, and the Lamb-mode-based Dirichlet-to-Neumann (DtN) conditions in the loss function. This formulation avoids derivative boundary constraints and enables direct extraction of modal amplitudes and far-field responses. The method is validated against an analytical multimodal solution for uniform shear-stress excitation. For representative single-frequency cases, the PINN reproduces the near-field displacement patterns and projected modal coefficients with good accuracy. A frequency-parameterized model is further trained to predict broadband responses and captures the main trends of the propagating modes with moderate errors over the considered band. The proposed model provides an extensible framework for broadband guided-wave forward modeling, with potential applications in transducer design optimization.
TL;DR: The results demonstrate that PINN achieves more accurate and stable full-field vibration reconstructions than conventional PINNs, particularly under conditions involving high-frequency modes, and highlights the potential of hybrid data-physics neural frameworks as an efficient and reliable approach for solving complex PDE-governed dynamical systems.
Physics-informed neural networks (PINNs) have emerged as a powerful paradigm in scientific machine learning by embedding governing physical laws into neural network training through loss functions. They have demonstrated remarkable success in solving various forward and inverse p…
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 sens…
Ultrasonic Guided Waves (UGWs) are among the most effective tools for damage diagnosis and Structural Health Monitoring (SHM) of thin-walled structures. However, traditional SHM methods based on UGWs typically require baseline measurements and signal post-processing to extract da…
TL;DR: Results obtained for concrete-filled steel tubular structures, based on seven-channel acceleration recordings sampled at 25 kHz, demonstrate that the proposed framework effectively enhances cross-domain stability in healthy–damage signal translation and suppresses abnormal frequency peaks.
In this study, a physics-informed Cycle-Consistent Generative Adversarial Network (CycleGAN) framework is proposed for vibration-based structural health monitoring of structures subjected to lateral impacts. The proposed method aims to translate vibration data between the healthy…
TL;DR: 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, allowing accurate estimation of system states and spatial health parameters without input force measurements.
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-dri…
Guided wave testing (GWT) is widely employed nowadays in the structural health monitoring (SHM) of plate-like and tubular components, offering long-range inspection capabilities and sensitivity to local geometric and material discontinuities. However, practical use remains challe…