mlsanityData to 7 Oct 2026

Learning 4DVAR inversion directly from observations

arXiv 2211.09741 ↗First posted 17 Nov 2022cs.LGcited by 3

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Abstract (arXiv)

Variational data assimilation and deep learning share many algorithmic aspects in common. While the former focuses on system state estimation, the latter provides great inductive biases to learn complex relationships. We here design a hybrid architecture learning the assimilation task directly from partial and noisy observations, using the mechanistic constraint of the 4DVAR algorithm. Finally, we show in an experiment that the proposed method was able to learn the desired inversion with interesting regularizing properties and that it also has computational interests.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 17 Nov 2022.

Builds on

8 of 21 references.

References · 8

  1. 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 290
  2. 2020Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solverscited by 201
  3. 2016Accelerating Eulerian Fluid Simulation With Convolutional Networkscited by 128
  4. 2017Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledgecited by 95
  5. 2018Stochastic seismic waveform inversion using generative adversarial networks as a geological priorcited by 47
  6. 2020Using machine learning to correct model error in data assimilation and forecast applicationscited by 40
  7. 2020Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximizationcited by 37
  8. 2017Machine Learning, Deepest Learning: Statistical Data Assimilation Problemscited by 13

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Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemblecited by 7
  2. 2024Combined Optimization of Dynamics and Assimilation with End-to-End Learning on Sparse Observationscited by 2
  3. 2025Neural Operator-Based Nonlinear Nudging for Chaotic Dynamical Systemscited by 1