Deep reinforcement learning (DRL) has emerged as a transformative approach in the realm of fluid dynamics, offering a data-driven framework to tackle the intrinsic complexities of active flow control.

Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.