A reinforcement learning strategy for p-adaptation in high order solvers
This work addresses efficiency and accuracy challenges in computational fluid dynamics simulations, representing an incremental improvement by applying existing RL methods to a specific domain.
The paper tackles the problem of optimizing polynomial order adaptation in high-order solvers for numerical simulations, using reinforcement learning to reduce computational cost and improve accuracy, as demonstrated on a 1D inviscid Burgers' equation.
Reinforcement learning (RL) has emerged as a promising approach to automating decision processes. This paper explores the application of RL techniques to optimise the polynomial order in the computational mesh when using high-order solvers. Mesh adaptation plays a crucial role in improving the efficiency of numerical simulations by improving accuracy while reducing the cost. Here, actor-critic RL models based on Proximal Policy Optimization offer a data-driven approach for agents to learn optimal mesh modifications based on evolving conditions. The paper provides a strategy for p-adaptation in high-order solvers and includes insights into the main aspects of RL-based mesh adaptation, including the formulation of appropriate reward structures and the interaction between the RL agent and the simulation environment. We discuss the impact of RL-based mesh p-adaptation on computational efficiency and accuracy. We test the RL p-adaptation strategy on a 1D inviscid Burgers' equation to demonstrate the effectiveness of the strategy. The RL strategy reduces the computational cost and improves accuracy over uniform adaptation, while minimising human intervention.