Developing methods to understand how emergent structures or properties arise spontaneously from system dynamics rather than being externally imposed, and how combinations of properties traditionally considered mutually exclusive can be achieved. We aim to enable predictive steering of such systems between macroscopic states and to identify the order parameters governing transitions between them under uncertainty and stochastic perturbations. We integrate complex systems theory with approaches such as control theory, scientific machine learning, and data-driven modelling.
Fig.: Phase space representation of how an emergent system is steered from an initial operating state to a new one. First, the system is pushed out of its existing operating point (i.e., the existing point is destabilized), then guided toward a new region in its phase space, and finally allowed to converge to a new state. Here, λ's represent control parameters, and x* denotes the system state. Figures taken from: https://arxiv.org/abs/2510.05344