How does stochastic differential equation interpretation apply to cost optimization?

Optimization can be modeled as a stochastic differential equation with noise-driven dynamics.

SGD can be interpreted as discretized Langevin dynamics where noise plays a role similar to thermal fluctuations in physics. The cost function acts as a potential energy surface, and optimization behaves like a particle moving under drift and diffusion forces. This explains why SGD can escape sharp minima.