A multigrid and neural network approach to reduce the computational cost of phi-FEM
arXiv:2605.137184.4
Predicted impact top 83% in NA · last 90 daysOriginality Synthesis-oriented
AI Analysis
For researchers using immersed boundary finite element methods, this work offers a cost-reduction strategy, but the improvements are incremental.
The paper combines multigrid and neural network methods to reduce the computational cost of phi-FEM while maintaining accuracy, demonstrated with 2D and 3D numerical tests.
In this work, we present a combination of a multigrid approach and the phi-FEM immersed boundary finite element method to reduce its computational cost while preserving its accuracy. To further reduce the numerical cost of the approach, we also propose the combination of the previous technique with some neural network methods. We illustrate the efficiency of these two approaches with numerical test cases in 2D and 3D.