NeuralPDE: Modelling Dynamical Systems from Data. (arXiv:2111.07671v2 [cs.LG] UPDATED)

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 …

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