Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures,
providing an expressive view of the chemical space and multiscale processes. Their …

The digitalization of chemical research and industry is vastly increasing the available data
for developing and parametrizing kinetic models. To exploit this data, machine learning …

In this work, neural ordinary differential equations (NODE) are used to identify
phenomenological growth rate functions to model the solid electrolyte interphase (SEI) …

Estimation of the rate of change of a system's states from state measurements is a key step
in several system analysis and model-building workflows. While numerous interpolating …

With the continuous development of the chemical industry, the concept of advocating green
development has become increasingly popular. Glycolic acid, serving as the monomer for …

We describe a framework that can integrate prior physical information, eg, the presence of
kinematic constraints, to support data-driven simulation in multibody dynamics. Unlike other …

This work investigates thermal enhancement for Casson magnetohydrodynamics (MHD)
hybrid nanofluid flow on an exponentially elongating surface. The flow is influenced by …

Data driven modelling and scientific machine learning have been responsible for significant
advances in determining suitable models to describe data. Within dynamical systems, neural …

This paper presents a versatile strategy for using simulations to identify control stages of
naphtha distillation plant where additional temperature probes can be installed. This search …

Chemical reaction neural networks (CRNNs) established as the state-of-the-art tool for
autonomous mechanism discovery. While they encode some fundamental physical laws …