  Meshing is a critical, but user-intensive process necessary for stable and
accurate simulations in computational fluid dynamics (CFD). Mesh generation is
often a bottleneck in CFD pipelines. Adaptive meshing techniques allow the mesh
to be updated automatically to produce an accurate solution for the problem at
hand. Existing classical techniques for adaptive meshing require either
additional functionality out of solvers, many training simulations, or both.
Current machine learning techniques often require substantial computational
cost for training data generation, and are restricted in scope to the training
data flow regime. MeshDQN is developed as a general purpose deep reinforcement
learning framework to iteratively coarsen meshes while preserving target
property calculation. A graph neural network based deep Q network is used to
select mesh vertices for removal and solution interpolation is used to bypass
expensive simulations at each step in the improvement process. MeshDQN requires
a single simulation prior to mesh coarsening, while making no assumptions about
flow regime, mesh type, or solver, only requiring the ability to modify meshes
directly in a CFD pipeline. MeshDQN successfully improves meshes for two 2D
airfoils.
