A dynamic system is typically described via a differential equation. This is an equation specifying the initial state of the system and the way it evolves through time. The solution to a differential equation is a function, which can always be written as an integral. Sometimes, the latter can be computed explicitly; this analytical expression is known as the explicit solution of the equation. In the presence of uncertainty, the dynamics of the system features stochastic processes, leading to a stochastic differential equation, the solution of which is no longer a deterministic function but is instead a stochastic integral. This is illustrated on a simple example modeling the evolution of the size of a population. Being a stochastic integral, the solution to a stochastic differential equation can be estimated numerically using the so-called Euler scheme, as explained in Chapter 13. This procedure is illustrated on the population size example. Finally, we study how the solution of a differential equation driven by a Brownian motion changes when switching from one probability measure to another thanks to Girsanov's theorem.