Engineers rely heavily on models to do the work they need to do. Whether it’s designing a new piece of equipment like a pump or a cooler, right through to modelling a full oil refinery to enable us to optimise its performance, models are essential.
Conventional engineering models are based on fundamental laws of the universe (e.g. the laws of thermodynamics, or Maxwell’s equations for electromagnetic phenonena) as well as empirical relationships based on experiment. They are very powerful and have helped us to build the world we live in today.
In recent years Machine Learning (a branch of artificial intelligence) has emerged as a new alternative for building models. This uses big data to develop purely empirical models of systems. It’s fundamentally a very different approach to conventional modelling.
There are two videos below. The first part discusses how modelling is done more generally. The second part will cover specifics of machine learning vs conventional modelling.