NeuralPDE: Modelling Dynamical Systems from Data

Understand

Many physical processes such as weather phenomena or fluid mechanics are governed by partial differential equations (PDEs).

• Modelling such dynamical systems using Neural Networks is an active research field.
• However, current methods are still very limited, as they do not exploit the knowledge about the dynamical nature of the system, require extensive prior knowledge about the governing equations or are limited to linear or first-order equations.
• In this work we make the observation that the Method of Lines used to solve PDEs can be represented using convolutions which makes convolutional neural networks (CNNs) the natural choice to parametrize arbitrary PDE dynamics.