Learning Mesh-Based Simulation with Graph Networks

Understand

Mesh-based simulations are central to modeling complex physical systems in many disciplines across science and engineering.

• Mesh representations support powerful numerical integration methods and their resolution can be adapted to strike favorable trade-offs between accuracy and efficiency.
• However, high-dimensional scientific simulations are very expensive to run, and solvers and parameters must often be tuned individually to each system studied.
• Here we introduce MeshGraphNets, a framework for learning mesh-based simulations using graph neural networks.