Time Dependent Density

Time-dependent density modeling focuses on representing and generating probability distributions that evolve over time. Current research emphasizes developing efficient algorithms, such as neural networks (including flow-based models and diffusion models), and optimal transport methods to accurately capture these dynamic densities. These advancements are crucial for improving generative modeling, addressing dataset bias, and enabling accurate simulations of physical systems like fluid dynamics and molecular interactions, ultimately leading to more robust and reliable machine learning applications and scientific modeling.