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Found 3 papers, 1 papers with code
Parallel bootstrap-based on-policy deep reinforcement learning for continuous flow control applications
The coupling of deep reinforcement learning to numerical flow control problems has recently received a considerable attention, leading to groundbreaking results and opening new perspectives for the domain.
Stabilized finite element method for incompressible solid dynamics using an updated Lagrangian formulation
This paper proposes a novel way to solve transient linear, and non-linear solid dynamics for compressible, nearly incompressible, and incompressible material in the updated Lagrangian framework for tetrahedral unstructured finite elements.
Numerical Analysis Numerical Analysis
Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows
This research gauges the ability of deep reinforcement learning (DRL) techniques to assist the optimization and control of fluid mechanical systems.
