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.

This system utilizes machine learning algorithms to optimize the operation of particle accelerators, reducing manual intervention and enhancing precision in real-time control. By integrating virtual ...

Australian researchers are proposing a novel, learning-based H∞ control method to enhance the performance of vanadium redox flow batteries in DC microgrids. Vanadium redox flow batteries are one of ...

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.

Intelligent organizations prioritize investments in machine learning and real-time data to improve decision making, accelerate revenue generation efforts, reduce operational expenses and protect ...

Editor’s Note: The SCM thesis Procurement Control Tower: Proof of Concept Through Machine Learning and Natural Language Processing was authored by Bishwajit Kumar and Pablo Barros Gomez, and ...

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 ...

Data quality is more important than ever, and many dataops teams struggle to keep up. Here are five ways to automate data operations with AI and ML. Data wrangling, dataops, data prep, data ...