Multi-fidelity enhanced few-shot time series prediction model for structural dynamics analysis
These days, deep learning (DL) techniques are regarded as an effective substitution of
refined finite element models to conduct structural dynamics analysis. Nevertheless, the …
refined finite element models to conduct structural dynamics analysis. Nevertheless, the …
Channel-attention-based LSTM network for modeling temperature-induced responses of cable-stayed bridges
Temperature has a significant impact on cable-stayed bridges, yielding structural responses
comparable to those from vehicular loads, winds, etc. However, advanced numerical …
comparable to those from vehicular loads, winds, etc. However, advanced numerical …
Geometry physics neural operator solver for solid mechanics
C Kaewnuratchadasorn, J Wang… - Computer‐Aided Civil …, 2025 - Wiley Online Library
Abstract This study developed Geometry Physics neural Operator (GPO), a novel solver
framework to approximate the partial differential equation (PDE) solutions for solid …
framework to approximate the partial differential equation (PDE) solutions for solid …
[HTML][HTML] Investigation on employment of time and frequency domain data for predicting nonlinear seismic responses of structures
This study examines methods of introducing time and frequency domain data for predicting
structures' nonlinear seismic responses. Ground motion records or time history structural …
structures' nonlinear seismic responses. Ground motion records or time history structural …
Deep learning enabled seismic fragility evaluation of structures subjected to mainshock-aftershock earthquakes
Mainshock-aftershock earthquakes have gained significant attention since accumulated
damages induced by multiple shocks are likely to cause failure of structures. This paper …
damages induced by multiple shocks are likely to cause failure of structures. This paper …