Latent Assimilation

Latent assimilation is a data assimilation technique aiming to improve the accuracy and efficiency of state estimation in complex systems by leveraging latent representations of the system's state and observations. Current research focuses on developing novel algorithms, such as deep Bayesian filters and diffusion models, to handle the non-linearity inherent in many real-world systems, often incorporating implicit neural representations for improved efficiency and handling of sparse data. These advancements are impacting diverse fields, from weather forecasting (e.g., storm surge prediction) to brain simulation and speech processing, by enabling more accurate and computationally feasible state estimation in high-dimensional systems with incomplete or noisy data.