Graph Neural Network enhanced Finite Element modelling

Neural network for constitutive modelling in finite element analysis

Finite element method has, in recent years, been widely used as a powerful tool in analysis of engineering problems. In this numerical analysis, the behavior of the actual material is approximated with that of an idealized material that deforms in accordance with some constitutive relationships. Therefore, the choice of an appropriate constitutive model, which adequately describes the behavior ...

Neural network representation of finite element method

A micromechanics-enhanced finite element formulation for modelling heterogeneous materials

In the analysis of composite materials with heterogeneous microstructures, full resolution of the heterogeneities using classical numerical approaches can be computationally prohibitive. This paper presents a micromechanics-enhanced finite element formulation that accurately captures the mechanical behaviour of heterogeneous materials in a computationally efficient manner. The strategy exploits...

Neural network time series forecasting of finite-element mesh adaptation

Basic learning algorithms and the neural network model are applied to the problem of mesh adaptation for the finite-element method for solving time-dependent partial differential equations. Time series prediction via the neural network methodology is used to predict the areas of ‘‘interest’’ in order to obtain an effective mesh refinement at the appropriate times. This allows for increased nume...

Finite-Element Modelling of Biotransistors

Current research efforts in biosensor design attempt to integrate biochemical assays with semiconductor substrates and microfluidic assemblies to realize fully integrated lab-on-chip devices. The DNA biotransistor (BioFET) is an example of such a device. The process of chemical modification of the FET and attachment of linker and probe molecules is a statistical process that can result in varia...