We present a framework for the multiscale modeling of finite strain magneto-elasticity based
on physics-augmented neural networks (NNs). By using a set of problem specific invariants …

Variationally consistent phase-field methods have been shown to be able to predict complex
three-dimensional crack patterns. However, current computational methodologies in the …

We propose a design of a metamaterial for magnetically tunable propagation of nonlinear
vector solitary waves. The metamaterial consists of a periodic array of units of hard-magnetic …

In the present work, the applicability of physics-augmented neural network (PANN)
constitutive models for complex electro-elastic finite element analysis is demonstrated. For …

Soft dielectric elastomers respond to electric stimuli by undergoing large deformations and
changes in their material properties. The actuation with deformable electrodes attached to …

This paper introduces a metamodelling technique that employs gradient-enhanced
Gaussian process regression (GPR) to emulate diverse internal energy densities based on …

Following the recent work of Bonet et al.(2015), this paper postulates a new convex multi-
variable variational framework for the analysis of Electro Active Polymers (EAPs) in the …

To mitigate the urban heat island (UHI) and release the low carbon potential of green walls,
we analyzed the cooling and energy-saving performance of different green wall designs …

Magneto-active polymers are polymer-based composites consisting of magnetizable micro-
particles embedded in an elastomeric matrix material. The presence of magnetic particles …

This paper introduces a metamodelling technique that leverages gradient-enhanced
Gaussian process regression (also known as gradient-enhanced Kriging), effectively …