Learning Neural PDE Solvers with Convergence Guarantees

Understand

Partial differential equations (PDEs) are widely used across the physical and computational sciences.

• Decades of research and engineering went into designing fast iterative solution methods.
• Existing solvers are general purpose, but may be sub-optimal for specific classes of problems.
• In contrast to existing hand-crafted solutions, we propose an approach to learn a fast iterative solver tailored to a specific domain.