Comparative assessment of physics-informed recurrent networks for modeling rate- and density-dependent compression in expanded polystyrene foams
Published online on July 28, 2025
Abstract
Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, Ahead of Print.
This study systematically compares Recurrent Neural Network architectures—namely simple Recurrent Neural Network, Long Short-Term Memory, and Gated Recurrent Units—for modeling the cyclic compressive mechanical response of Expanded Polystyrene foam across ...
This study systematically compares Recurrent Neural Network architectures—namely simple Recurrent Neural Network, Long Short-Term Memory, and Gated Recurrent Units—for modeling the cyclic compressive mechanical response of Expanded Polystyrene foam across ...