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Found 5 papers, 5 papers with code
Beacon, a lightweight deep reinforcement learning benchmark library for flow control
Recently, the increasing use of deep reinforcement learning for flow control problems has led to a new area of research, focused on the coupling and the adaptation of the existing algorithms to the control of numerical fluid dynamics environments.
U-net architectures for fast prediction of incompressible laminar flows
Machine learning is a popular tool that is being applied to many domains, from computer vision to natural language processing.
Computational Physics Image and Video Processing
Direct shape optimization through deep reinforcement learning
Deep Reinforcement Learning (DRL) has recently spread into a range of domains within physics and engineering, with multiple remarkable achievements.
Computational Engineering, Finance, and Science
A review on Deep Reinforcement Learning for Fluid Mechanics
In this work, we conduct a detailed review of existing DRL applications to fluid mechanics problems.
A supervised neural network for drag prediction of arbitrary 2D shapes in low Reynolds number flows
Despite the significant breakthrough of neural networks in the last few years, their spreading in the field of computational fluid dynamics is very recent, and many applications remain to explore.
Computational Physics
