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.

Reinforcement learning (RL) represents a paradigm shift in process control, offering adaptive and data‐driven strategies for the management and optimisation of complex industrial processes. By ...

In the recent past, you probably attended a virtual lunch-and-learn presentation, read an article, or had a discussion with a controls sales representative in which the topic was a chilled water plant ...

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 ...

Download PDF More Formats on IMF eLibrary Order a Print Copy Create Citation The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A ...

The integrated AI approach for bipedal locomotion combines physics-driven planning and reinforcement learning, achieving ...

The study reveals a rapidly evolving field where AI plays a pivotal role in accelerating design, enabling predictive maintenance, optimizing control systems, and powering intelligent digital replicas ...