Neural Information Processing Systems http://nips.cc/

These methods can be broadly categorized into two types: function learning and operator learning approaches. In function learning, the goal is to directly learn the solution.

Fourier Neural Operators (FNOs) have shown promise for solving partial differential equations (PDEs).

Inthis paper, we present GRAPHSPLINENETS, a novel deep-learning method to speed up the forecasting of physical systems by reducing the grid size and number of iteration steps of deep surrogate models. Our method uses two differentiable orthogonal spline collocation methods to efficiently predict response at any location in time and space. Additionally, we introduce an adaptive collocation strategy in space to prioritize sampling from the most important regions.