Simulation & Modelling (SM)
Modelling is the very core of data analytics. Modelling enables inferences to be made from data and support decision making. Modelling is very diverse area encompassing a number of different disciplines. In Computer Science modelling makes use of principles from the Mathematics, Statistics and Artificial Intelligence disciplines. Very often in Data Analytics a model that is constructed from the data group up, meaning that no pre-conceived ideas about the statistical distribution is made in advance. This enables models that are developed to generalize better to new incoming data than traditional method that assume an Apriori parametric statistical distribution.
Simulation is a concept that is closely connected to Modelling. A system that is to be studied is assumed to have basic behavioural properties and the system is studied by implementing a computer program that mimics the expected behaviour with the data that is available. Simulation can be used to discover system with various different types of data. For example we can simulate the behaviour of a Robot in navigating unfamiliar terrain that does not have a topological map.
Both Modelling and Simulation are well established areas in Data Analytics but new research is necessary to construct models and simulations for new types of systems that are constantly being created in today’s world.
Simulation and modelling approaches are widely used in problem solving. The value of this approach is its ability to transform a conceptual, mathematical or analytical model into a computational simulation model which can test hypotheses, collect data, analyse drivers, and generate emergent patterns. Computational simulations provide benefits as it can enable creation of an artificial representation of a system. SM approaches are flexible and capable of dealing with artificial or real-data sets to produce results that comprise emergent phenomenon.
At DSRG we employ hybrid simulation approaches that combine a range of analytical, mathematical, computational, and simulation techniques to investigate research problems.
Our focus area includes:
- Agent based modelling
- Artificial intelligence in healthcare
- Creation of dynamic networks for testing influence
- Network implementation and technology uptake