Speaker
Description
Reduced-order models offer computationally efficient approximations of
complex systems, enabling multi-query tasks in design and optimisation with
low cost and sufficient accuracy. Data-driven strategies are particularly
appealing when underlying models are inaccessible or too expensive to
evaluate, and recent advances in AI-based architectures have naturally
entered this space. However, these architectures still face challenges when
confronted with systems exhibiting variable dynamics, bifurcations, or
chaotic behaviour. In this talk, we present a shift in perspective that
unifies complex dynamical systems with nonintrusive, data-driven
reduced-order modelling approaches, thereby broadening the range of
applications that can be addressed effectively.