Diffusion SDE

Diffusion stochastic differential equations (SDEs) model continuous-time systems with inherent randomness, finding applications in diverse fields like reinforcement learning, image generation, and biological modeling. Current research focuses on developing efficient algorithms for solving these SDEs, including novel numerical solvers and generative model architectures such as neural SDEs and those incorporating measure-preserving dynamics, to improve sampling speed and quality. This active area of research is driving advancements in various applications, from enhancing the performance of generative models and accelerating inverse problems to providing more accurate and interpretable models of complex dynamic systems.