Reduced-Order Autodifferentiable Ensemble Kalman Filters
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 mo...
Saved in:
Main Authors | , , |
---|---|
Format | Journal Article |
Language | English |
Published |
27.01.2023
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | 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 ensemble
Kalman filter 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. |
---|---|
DOI: | 10.48550/arxiv.2301.11961 |