Events

Toward consistent data assimilation with structured generative models

Speaker: Ricardo Baptista

Location: 60 Fifth Avenue, Room 7th floor common area

Date: Wednesday, March 6, 2024

Accurate state estimation, also known as data assimilation, is essential for geophysical forecasts, ranging from numerical weather prediction to long-term climate studies. While ensemble Kalman methods are widely adopted for this task in high dimensions, these methods are inconsistent at capturing the true uncertainty in non-Gaussian settings. In this presentation, I will introduce a scalable framework for consistent data assimilation. First, I will demonstrate how inference methods based on conditional generative models generalize ensemble Kalman methods and correctly characterize the probability distributions in nonlinear filtering problems. Second, I will present a dimension reduction approach for limited data settings by identifying and encoding low-dimensional structure in generative models with guarantees on the approximation error. The benefits of this framework will be showcased in applications from fluid mechanics with chaotic dynamics, where classic methods are unstable in small sample regimes.