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 …

The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

We extend the scope of our recently developed approach for unsupervised automated
discovery of material laws (denoted as EUCLID) to the general case of a material belonging …

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 …

Identifying constitutive parameters in engineering and biological materials, particularly those
with intricate geometries and mechanical behaviors, remains a longstanding challenge. The …

Abstract A Physics-Informed Neural Network (PINN) model is developed to extract material
behavior from full-field displacement data. The PINN model consists of independent …

Nowadays, strain and displacement can be measured using techniques such as electronic
speckle pattern interferometry and digital image correlation. However, usually, only some …

This work presents the application of Physics Informed Deep Learning models for both
surrogate modelling and segmentation of composite materials. The segmentation is …

Identifying constitutive parameters in engineering and biological materials, particularly those
with intricate geometries and mechanical behaviors, remains a longstanding challenge. The …

The non-contact transmission product permanent magnet coupler (PMC) has been widely
used in industry due to its advantages such as low noise and vibration, high efficiency, high …