Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Variational Data Assimilation with a Learned Inverse Observation Operator
Authors: Thomas Frerix, Dmitrii Kochkov, Jamie Smith, Daniel Cremers, Michael Brenner, Stephan Hoyer
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results for the Lorenz96 model and a two-dimensional turbulent fluid flow demonstrate that this procedure significantly improves forecast quality for chaotic systems. |
| Researcher Affiliation | Collaboration | 1Google Research 2Technical University of Munich 3Harvard University. Correspondence to: Thomas Frerix <EMAIL>, Stephan Hoyer <EMAIL>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://github.com/googleinterns/ invobs-data-assimilation |
| Open Datasets | No | We train on a dataset of 32000 independent observation trajectories with batch size 8 for 500 epochs. (No access information provided for this dataset.) |
| Dataset Splits | No | The paper mentions a training dataset and test trajectories ('on a set of 100 test trajectories') but does not specify a separate validation split or its size. |
| Hardware Specification | Yes | All models can be trained and optimized on a single NVIDIA V100 GPU. |
| Software Dependencies | No | The paper mentions using JAX, Flax, and the Adam optimizer but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We implement our models for the approximate inverse in JAX (Bradbury et al., 2018) and use Flax as neural network library, with the Adam optimizer (Kingma & Ba, 2015) and learning rate of 10 3 for training1... We train on a dataset of 32000 independent observation trajectories with batch size 8 for 500 epochs. We use L-BFGS (Nocedal & Wright, 2006) as an optimizer for assimilation, retaining a history of 10 vectors for the Hessian approximation. |