Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …

This research aims to study and assess state-of-the-art physics-informed neural networks
(PINNs) from different researchers' perspectives. The PRISMA framework was used for a …

A physics informed neural network (PINN) incorporates the physics of a system by satisfying
its boundary value problem through a neural network's loss function. The PINN approach …

Near-wall blood flow and wall shear stress (WSS) regulate major forms of cardiovascular
disease, yet they are challenging to quantify with high fidelity. Patient-specific computational …

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 …

We propose a hybrid neural network (NN) and PDE approach for learning generalizable
PDE dynamics from motion observations. Many NN approaches learn an end-to-end model …

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

Data-driven methods have changed the way we understand and model materials. However,
while providing unmatched flexibility, these methods have limitations such as reduced …

Data-driven methods are becoming an essential part of computational mechanics due to
their advantages over traditional material modeling. Deep neural networks are able to learn …

Cement-based grouting has been widely used in mining engineering; its constitutive law has
not been comprehensively studied. In this study, a novel constitutive law of cement-grouted …