For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …

The incorporation of physical information in machine learning frameworks is opening and
transforming many application domains. Here the learning process is augmented through …

Elastography is a medical imaging technique used to measure the elasticity of tissues by
comparing ultrasound signals before and after a light compression. The lateral resolution of …

For solving the computational solid mechanics problems, despite significant advances have
been achieved through the numerical discretization of partial differential equations (PDEs) …

This paper proposes a framework for physics-informed neural networks (PINNs) in the
nonlinear bending of 3D functionally graded (FG) beams. Utilizing the underlying physical …

We propose a novel approach for tackling scientific problems governed by differential
equations, based on the concept of a physics-informed neural networks (PINNs). The …

Abstract 3D/4D printing offers significant flexibility in manufacturing complex structures with
a diverse range of mechanical responses, while also posing critical needs in tackling …

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

The development of biophysical models for clinical applications is rapidly advancing in the
research community, thanks to their predictive nature and their ability to assist 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 …