A review on deep reinforcement learning for fluid mechanics

DOI10.1016/j.compfluid.2021.104973OpenAlexW2967095864MaRDI QIDQ2245392

Alexander Kuhnle, Aurélien Larcher, Jonathan Viquerat, Paul Garnier, Jean Rabault, Elie Hachem

Publication date: 15 November 2021

Published in: Computers and Fluids (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1908.04127

zbMATH Keywords

fluid mechanicsdeep reinforcement learning

Mathematics Subject Classification ID

Fluid mechanics (76-XX)

Related Items (12)

Comparative analysis of machine learning methods for active flow control ⋮ Dynamic metasurface control using deep reinforcement learning ⋮ Deep reinforcement learning of viscous incompressible flow ⋮ Accelerating high order discontinuous Galerkin solvers using neural networks: 1D Burgers' equation ⋮ A deep reinforcement learning framework for dynamic optimization of numerical schemes for compressible flow simulations ⋮ StressD: 2D stress estimation using denoising diffusion model ⋮ Accelerating high order discontinuous Galerkin solvers using neural networks: 3D compressible Navier-Stokes equations ⋮ Near-grazing bifurcations and deep reinforcement learning control of an impact oscillator with elastic constraints ⋮ Multi-fidelity reinforcement learning framework for shape optimization ⋮ Reinforcement-learning-based control of confined cylinder wakes with stability analyses ⋮ Reinforcement-learning-based control of convectively unstable flows ⋮ Reinforcement-learning-based actuator selection method for active flow control

Uses Software

• FEniCS
• OpenAI Gym
• GitHub
• Pluribus
• Stable Baselines

Cites Work

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• An analysis of temporal-difference learning with function approximation
• Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control
• Optimization Methods for Large-Scale Machine Learning
• Control of chaotic systems by deep reinforcement learning
• Superhuman AI for multiplayer poker

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