Stochastic Differential Equations (SDEs) are mathematical equations that describe systems influenced by random processes. They extend traditional differential equations by incorporating noise, allowing for the modeling of unpredictable phenomena in various fields, such as finance, physics, and biology. In SDEs, the solution is often a stochastic process, meaning it evolves over time with inherent randomness. This makes them useful for simulating real-world scenarios, like stock price movements or population dynamics, where uncertainty plays a crucial role. Key concepts related to SDEs include Itô calculus and Brownian motion, which help in analyzing and solving these equations.