The combination of food chemistry and computational simulation has brought many impacts
to food research, moving from experimental chemistry to computer chemistry. This paper will …

This paper presents a Physics-Informed Neural Network-based (PINN-based) surrogate
framework, which can couple time-based moisture concentration and moisture-content …

Predicting microscale mechanisms of plant-based food materials has been an enduring
challenge due to the inherent complexity of involved physics and prohibitively-high …

This paper introduces a novel Physics-Informed Neural Network-based (PINN-based) multi-
domain computational framework to analyse nonlinear and heterogeneous morphological …

In recent years, modelling techniques have become more frequently adopted in the field of
food processing, especially for cereal-based products, which are among the most consumed …

The rheological properties of emerging novel complex fluids are usually governed by
multiple variables, which is challenging for traditional parameterized rheological models in …

Numerical modelling has emerged as a powerful and effective tool to study various dynamic
behaviours of biological matter. Such numerical modelling tools have contributed to the …

Several open software packages have popularized modeling and simulation strategies at
the food product scale. Food processing and key digestion steps can be described in 3D …

A better understanding of plant cell micromechanics would enhance the current opinion on
“how things are happening” inside a plant cell, enabling more detailed insights into plant …

Recently, meshfree-based computational modelling approaches have become popular in
modelling biological phenomena due to their superior ability to simulate large deformations …