Score-based Data Assimilation
Authors: François Rozet, Gilles Louppe
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 francois.rozet@uliege.be Gilles Louppe University of Liège g.louppe@uliege.be |
| 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) |