DynAMO: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws

Dzanic, T.; Mittal, K.; Kim, D.; Yang, J.; Petrides, S.; Keith, B.; Anderson, R.

doi:10.1016/j.jcp.2024.112924

Journal of Computational Physics

2024

DOI: 10.1016/j.jcp.2024.112924

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DynAMO: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws

T. Dzanic,

K. Mittal,

D. Kim

et al.

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Deep Reinforcement Learning for Fluid Mechanics: Control, Optimization, and Automation

Kim,

Jeon,

Chae

et al. 2024

Fluids

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A comprehensive review of recent advancements in applying deep reinforcement learning (DRL) to fluid dynamics problems is presented. Applications in flow control and shape optimization, the primary fields where DRL is currently utilized, are thoroughly examined. Moreover, the review introduces emerging research trends in automation within computational fluid dynamics, a promising field for enhancing the efficiency and reliability of numerical analysis. Emphasis is placed on strategies developed to overcome challenges in applying DRL to complex, real-world engineering problems, such as data efficiency, turbulence, and partial observability. Specifically, the implementations of transfer learning, multi-agent reinforcement learning, and the partially observable Markov decision process are discussed, illustrating how these techniques can provide solutions to such issues. Finally, future research directions that could further advance the integration of DRL in fluid dynamics research are highlighted.

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Deep Reinforcement Learning for Fluid Mechanics: Control, Optimization, and Automation

Kim,

Jeon,

Chae

et al. 2024

Fluids

View full text Add to dashboard Cite

show abstract

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DynAMO: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws

Cited by 1 publication

References 37 publications

Deep Reinforcement Learning for Fluid Mechanics: Control, Optimization, and Automation

Deep Reinforcement Learning for Fluid Mechanics: Control, Optimization, and Automation

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