Learning Differential Equations that are Easy to Solve

Understand

Differential equations parameterized by neural networks become expensive to solve numerically as training progresses.

• We propose a remedy that encourages learned dynamics to be easier to solve.
• Specifically, we introduce a differentiable surrogate for the time cost of standard numerical solvers, using higher-order derivatives of solution trajectories.
• These derivatives are efficient to compute with Taylor-mode automatic differentiation.