  We present a novel approach to automate and optimize anisotropic p-adaptation
in high-order h/p solvers using Reinforcement Learning (RL). The dynamic RL
adaptation uses the evolving solution to adjust the high-order polynomials. We
develop an offline training approach, decoupled from the main solver, which
shows minimal overcost when performing simulations. In addition, we derive a
RL-based error estimation approach that enables the quantification of local
discretization errors. The proposed methodology is agnostic to both the
computational mesh and the partial differential equation being solved.
  The application of RL to mesh adaptation offers several benefits. It enables
automated, adaptive mesh refinement, reducing the need for manual intervention.
It optimizes computational resources by dynamically allocating high-order
polynomials where necessary and minimizing refinement in stable regions. This
leads to computational cost savings while maintaining solution accuracy.
Furthermore, RL allows for the exploration of unconventional mesh adaptations,
potentially enhancing the accuracy and robustness of simulations. This work
extends our original research, offering a more robust, reproducible, and
generalizable approach applicable to complex three-dimensional problems. We
provide validation for laminar and turbulent cases: circular cylinders, Taylor
Green Vortex and a 10MW wind turbine to illustrate the flexibility of the
proposed approach.
