In the framework of solid mechanics, the task of deriving material parameters from
experimental data has recently re-emerged with the progress in full-field measurement …

Spiking neural networks (SNN), also often referred to as the third generation of neural
networks, carry the potential for a massive reduction in memory and energy consumption …

In this study, we propose a method to infer the displacement and stress of the entire domain
using physics-informed neural networks (PINNs), utilizing locally measured strain data from …

The deflection dynamic load allowance (DLA) of stiff bridges with high piers requires sub-
millimeter accuracy. New technologies such as the vision-based optical method and GNSS …

The Statistical Finite Element Method (statFEM) offers a Bayesian framework for integrating
computational models with observational data, thus providing improved predictions for …

Conventional simulation methods, such as the Finite Element method, are less suitable for
efficient and adaptive digital twins of large-scale structures needed for structural health …

Predictive modeling involving simulation and sensor data at the same time, is a growing
challenge in computational science. Even with large-scale finite element models, a …

Penelitian ini menyelidiki dampak penggunaan media sosial pada remaja di Jakarta,
dengan fokus pada interaksi online, pengaruh dukungan, dampak psikologis, perilaku …

This research investigates the impact of Weather Prediction, Resource Management, and
Land Optimization on the adoption of Big Data Analytics in agricultural land utilization within …

In recent years, the use of simulation-based digital twins for monitoring and assessment of
complex mechanical systems has greatly expanded. Their potential to increase the …