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.

AI can be added to legacy motion control systems in three phases with minimal disruption: data collection via edge gateways, non-interfering anomaly detection and supervisory control integration.

This illustration draws a parallel between quantum state tomography and natural language modeling. In quantum tomography, structured measurements yield probability outcomes that are aggregated to ...