Introduction to SDEs

A stochastic differential equation (SDE) is a differential equation in which one or more of the terms is a stochastic process,[1] resulting in a solution which is also a stochastic process. SDEs have many applications throughout pure mathematics and are used to model various behaviours of stochastic models such as stock prices,[2] random growth models[3] or physical systems that are subjected to thermal fluctuations.

SDEs have a random differential that is in the most basic case random white noise calculated as the derivative of a Brownian motion or more generally a semimartingale. However, other types of random behaviour are possible, such as jump processes like Lévy processes[4] or semimartingales with jumps. Random differential equations are conjugate to stochastic differential equations. Stochastic differential equations can also be extended to differential manifolds.[5][6][7][8]

Brownian Motion

Brownian motion is the random motion of particles suspended in a medium (a liquid or a gas).[2]

This motion pattern typically consists of random fluctuations in a particle’s position inside a fluid sub-domain, followed by a relocation to another sub-domain. Each relocation is followed by more fluctuations within the new closed volume. This pattern describes a fluid at thermal equilibrium, defined by a given temperature. Within such a fluid, there exists no preferential direction of flow (as in transport phenomena). More specifically, the fluid’s overall linear and angular momenta remain null over time. The kinetic energies of the molecular Brownian motions, together with those of molecular rotations and vibrations, sum up to the caloric component of a fluid’s internal energy (the equipartition theorem).