Models are the fundamental structures required to make sense of data and systems. Under this theme we develop the new models, both stochastic and statistical in nature, required by new problems and to support Challenging Data.
From nearly 17,000 measurements of coral position and growth on a reef in French Polynesia, an international research team teased out which reef-building species were most sensitive to attack from crown-of-thorns starfish and destruction during tropical cyclones, and what times of their life history.
This research deals with the field of symbolic data analysis. Researchers have developed ways of estimating how components of the underlying data mix and interact to produce the values of the symbols they observe. So the models they build relate more closely to what's really happening.
Rare events such as the state-wide power blackout in South Australia in September 2016, natural disasters such as floods and bushfires, or the ensuing chaos when parts of a complex interconnected systems such as the internet fail, are difficult for researchers to simulate or model. They're the primary interest of ACEMS Chief Investigator Dirk Kroese.
Everybody is different, and every body is different. Significant variability is a common feature of all of the physiological systems that compose the function of the human body, and understanding this variability is critical to explaining differences in susceptibility to pathological conditions, and also to explaining how medical treatments can potentially succeed or fail even when applied to treat the same condition.
Fundamental models of interacting particles, such as those occurring in mathematical physics and queuing theory, are widely studied to understand non-equilibrium behavior in physical systems consisting of large numbers of particles, to study large classes of transport phenomena, scheduling mechanisms and interface growth.
Investigation and development of virtual log models for Southern Pines will be based on analysis of data from the cores, peeled billets and approximately 60 sawn logs. We plan to predict log and stem wood properties from the breast height cores taken in the field study.
The broad aim to develop mathematical models for population dynamics that account for local population behaviour, individual variation, spatial structure, and differing migration patterns, and to calibrate these models to real data.