Organizers: Marcus Weber

Abstract:

In an era defined by data abundance and complexity, the question of where to sample becomes as critical as how. This minisymposium explores the nuanced interplay between spatial, temporal, and structural dimensions in sampling strategies for complex systems. Speakers will examine adaptive, probabilistic, and machine learning–driven approaches that optimize representativeness, efficiency, and insight. Emphasis will be placed on the challenges of heterogeneity, uncertainty, and evolving system behavior. The symposium aims to foster cross-disciplinary dialogue and spark innovation in the artful science of sampling.