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 [1].
Continuous PDE Dynamics Forecasting with Implicit Neural Representations
Authors: Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi, Alain Rakotomamonjy, patrick gallinari
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate DINO s versatility and state-of-the-art performance versus prior neural PDE forecasters, representative of grid, operator and INR-based methods, via thorough experiments on challenging multi-dimensional PDEs in various spatiotemporal generalization settings. |
| Researcher Affiliation | Collaboration | 1Sorbonne Université, CNRS, ISIR, F-75005 Paris, France 2Criteo AI Lab, Paris |
| Pseudocode | Yes | Algorithm 1: DINO pseudo-code |
| Open Source Code | Yes | We provide our source code at https://github.com/ mkirchmeyer/DINo. |
| Open Datasets | Yes | We evaluate DINO on real-world data to further assess its applicability. Following de Bézenac et al. (2018) and Donà et al. (2021), we model the Sea Surface Temperature (SST) of the Atlantic ocean, derived from the data-assimilation engine NEMO (Nucleus for European Modeling of the Ocean, Madec & NEMO System Team) using E.U. Copernicus Marine Service Information.1 https://data.marine.copernicus.eu/product/GLOBAL_ANALYSIS_FORECAST_ PHY_001_024/description. |
| Dataset Splits | No | The paper specifies train and test splits (e.g., '512 train trajectories and 32 test trajectories'), but does not explicitly mention or define a separate validation set or split percentages for reproduction. |
| Hardware Specification | Yes | This represents 2.76 s for 64 trajectories on a single Tesla V100 Nvidia GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2019)' and 'Torch Diff Eq (Chen et al., 2018)', but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We list the hyperparameters of DINO for each dataset in Table 7. |