Machine learning techniques to construct patched analog ensembles for data assimilation

DOI10.1016/J.JCP.2021.110532OpenAlexW3134009298MaRDI QIDQ2132612

L. Minah Yang, Ian Grooms

Publication date: 28 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2103.00318

zbMATH Keywords

machine learningdata assimilationanalogs

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Approximation methods and numerical treatment of dynamical systems (37Mxx) Computer science (68-XX)

Related Items (3)

A data-driven, physics-informed framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings ⋮ Reduced-order autodifferentiable ensemble Kalman filters ⋮ Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems

Uses Software

• LLNet
• Unnamed Item

Cites Work

• LLNet
• Analog forecasting with dynamics-adapted kernels
• An Introduction to Variational Autoencoders
• Data Assimilation

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