Adaptive Meshing

Adaptive meshing optimizes the distribution of computational elements in numerical simulations to improve accuracy and efficiency. Current research focuses on integrating machine learning, particularly graph neural networks and deep reinforcement learning, with traditional methods like finite element analysis to automate mesh refinement and relocation, often guided by expert demonstrations or error minimization. This approach promises significant advancements in various fields, including computational fluid dynamics and image segmentation, by accelerating simulations and enhancing the accuracy of solutions for complex problems. The resulting improvements in computational efficiency and solution quality have broad implications across scientific computing and engineering applications.