Inverse-designed growth-based cellular metamaterials

Advancements in machine learning have sparked significant interest in designing mechanical metamaterials, i.e., materials that derive their properties from their inherent microstructure rather than just their constituent material. We propose a data-driven exploration of the design space of growth-based cellular metamaterials based on star-shaped...

Coevolution of machine learning and process-based modelling to revolutionize Earth and environmental sciences: A perspective

Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process-based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support....

Three-Dimensional Sediment Dynamics in Well-Mixed Estuaries: Importance of the Internally Generated Overtide, Spatial Settling Lag, and Gravitational Circulation

To investigate the dominant sediment transport and trapping mechanisms, a semi-analytical three-dimensional model is developed resolving the dynamic effects of salt intrusion on sediment in well-mixed estuaries in morphodynamic equilibrium. As a study case, a schematized estuary with a converging width and a channel-shoal structure...

Response of large-scale coastal basins to wind forcing: Influence of topography

Because wind is one of the main forcings in storm surge, we present an idealised process-based model to study the influence of topographic variations on the frequency response of large-scale coastal basins subject to time-periodic wind forcing. Coastal basins are represented by a semi-enclosed rectangular inner region forced by wind. It is...