3 papers
cs.LG2021
Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components
Alexander Wikner, Jaideep Pathak, Brian R. Hunt +3
We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is in the form of noisy partial measurements of the past and present state…
cs.LG2020
Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems
Alexander Wikner, Jaideep Pathak, Brian Hunt +5
We consider the commonly encountered situation (e.g., in weather forecasting) where the goal is to predict the time evolution of a large, spatiotemporally chaotic dynamical system…
physics.data-an2005
Efficient Data Assimilation for Spatiotemporal Chaos: a Local Ensemble Transform Kalman Filter
Brian R. Hunt, Eric J. Kostelich, Istvan Szunyogh
Data assimilation is an iterative approach to the problem of estimating the state of a dynamical system using both current and past observations of the system together with a model…