Reduced-order autodifferentiable ensemble Kalman filters
Abstract
This paper introduces a computational framework to reconstruct and forecast a partially observed state that evolves according to an unknown or expensive-to-simulate dynamical system. Our reduced-order autodifferentiable ensemble Kalman filters (ROAD-EnKFs) learn a latent low-dimensional surrogate model for the dynamics and a decoder that maps from the latent space to the state space. The learned dynamics and decoder are then used within an EnKF to reconstruct and forecast the state. Numerical experiments show that if the state dynamics exhibit a hidden low-dimensional structure, ROAD-EnKFs achieve higher accuracy at lower computational cost compared to existing methods. If such structure is not expressed in the latent state dynamics, ROAD-EnKFs achieve similar accuracy at lower cost, making them a promising approach for surrogate state reconstruction and forecasting.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 27, 2023
- Source ID
- 10.1088/1361-6420/acff14
Entities
People
- Daniel Sanz-Alonso
- Rebecca Willett
- Yuming Chen
Organizations
- National Science Foundation
- United States Department of Defense
- United States Department of Energy