Mechanistic models have been used for centuries to describe complex interconnected
processes, including biological ones. As the scope of these models has widened, so have …

Computational models are not just appealing because they can simulate and predict the
development of biological phenomena across multiple spatial and temporal scales, but also …

The brain has effectively proven a powerful inspiration for the development of computing
architectures in which processing is tightly integrated with memory, communication is event …

In the field of energy system modelling, increasing complexity and optimization analysis are
essential for understanding the most effective decarbonization options. However, the …

We developed a high-resolution machine learning based surrogate model to identify a
robust land-use future for Australia which meets multiple UN Sustainable Development …

Abstract Machine learning models effectively forecast and improve engineering systems as
solar dryers, making them valuable replacements for traditional physics-based models. Also …

The modelling of many real-world problems relies on computationally heavy simulations of
randomly interacting individuals or agents. However, the values of the parameters that …

Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of
complex, dynamic infections under varying conditions and navigate uncertain environments …

Here we propose a detailed protocol to enable an accelerated inverse design of acoustic
coatings for underwater sound attenuation application by coupling Machine Learning and …

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy
sources, including biomass, biofuels, engines, and solar power, can revolutionize the …