Adaptive Mesh Refinement

Adaptive Mesh Refinement (AMR) is a computational technique that dynamically adjusts the density of a mesh used in simulations, focusing computational resources on areas requiring higher resolution while maintaining efficiency. Current research emphasizes the use of machine learning, particularly deep reinforcement learning and graph neural networks, to automate and optimize mesh adaptation, often formulating the problem as a multi-agent system or Markov decision process. This approach promises significant improvements in the accuracy and speed of simulations across diverse fields like computational fluid dynamics and 3D modeling, reducing computational costs and enabling more complex simulations.