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 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 …

Designing of intrusion detection system (IDS), and mobile ad hoc networks (MANET)
prevention technique with examined detection rate, memory consumption with minimum …

For more than 100 years, chemical, physical, and material scientists have proposed
competing constitutive models to best characterize the behavior of natural and man-made …

In the present work, a hyperelastic constitutive model based on neural networks is proposed
which fulfills all common constitutive conditions by construction, and in particular, is …

Recently, a class of machine learning methods called physics-informed neural networks
(PINNs) has been proposed and gained prevalence in solving various scientific computing …

Physics informed neural networks (PINNs) are capable of finding the solution for a given
boundary value problem. Here, the training of the network is equivalent to the minimization …

In the present work, two machine learning based constitutive models for finite deformations
are proposed. Using input convex neural networks, the models are hyperelastic, anisotropic …

In contemporary elasticity theory, the strain–energy function predominantly relies on the first
invariant I 1 of the deformation tensor; a practice that has been influenced by models derived …

The brain is our softest and most vulnerable organ, and understanding its physics is a
challenging but significant task. Throughout the past decade, numerous competing models …