  Neural Laplace is a unified framework for learning diverse classes of
differential equations (DE). For different classes of DE, this framework
outperforms other approaches relying on neural networks that aim to learn
classes of ordinary differential equations (ODE). However, many systems can't
be modelled using ODEs. Stochastic differential equations (SDE) are the
mathematical tool of choice when modelling spatiotemporal DE dynamics under the
influence of randomness. In this work, we review the potential applications of
Neural Laplace to learn diverse classes of SDE, both from a theoretical and a
practical point of view.
