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..
Score-based Data Assimilation
Authors: François Rozet, Gilles Louppe
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present theoretical and empirical evidence supporting the effectiveness of our method. We demonstrate the effectiveness of score-based data assimilation on two chaotic dynamical systems: the Lorenz 1963 [47] and Kolmogorov flow [48] systems. |
| Researcher Affiliation | Academia | François Rozet University of Liège EMAIL Gilles Louppe University of Liège EMAIL |
| Pseudocode | Yes | Algorithm 1 Training ϵϕ(xi k:i+k(t), t) |
| Open Source Code | Yes | The code for all experiments is made available at https://github.com/francois-rozet/sda. |
| Open Datasets | No | We generate 1024 independent trajectories of 1024 states, which are split into training (80 %), validation (10 %) and evaluation (10 %) sets. |
| Dataset Splits | Yes | We generate 1024 independent trajectories of 1024 states, which are split into training (80 %), validation (10 %) and evaluation (10 %) sets. |
| Hardware Specification | No | Experiments were conducted with the help of a cluster of GPUs. In particular, score networks were trained and evaluated concurrently, each on a single GPU with at least 11 GB of memory. |
| Software Dependencies | No | No specific version numbers are provided for software dependencies like jax-cfd or the optimizers and activation functions used. |
| Experiment Setup | Yes | Architecture and training details for each k are provided in Appendix D. (e.g., from Table 1: Weight decay 10^-3, Learning rate 10^-3, Batch size 256) |