Deep Reinforcement Learning for Computational Fluid Dynamics

Description

Deep Reinforcement Learning (DRL) is revolutionizing the way we approach computational problems, and in this course, we will show you how to apply this powerful technique to Computational Fluid Dynamics (CFD). Learn how DRL can be used to optimize fluid flow simulations, control turbulent flows, and enhance CFD model accuracy. This course provides both theoretical insights and hands-on experience, guiding you through the process of setting up a DRL framework for CFD applications. You will gain the skills necessary to design and train agents for real-world engineering problems like aerodynamic flow control and heat transfer optimization.