Self-Supervised Coarsening of Unstructured Grid with Automatic Differentiation
Authors: Sergei Shumilin, Alexander Ryabov, Nikolay Yavich, Evgeny Burnaev, Vladimir Vanovskiy
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate performance of the designed algorithm on two PDEs: a linear parabolic equation which governs slightly compressible fluid flow in porous media and the wave equation. Our results show that in the considered scenarios, we reduced the number of grid points up to 10 times while preserving the modeled variable dynamics in the points of interest. |
| Researcher Affiliation | Academia | 1Applied AI Center, Skolkovo Institute of Science and Technology, Moscow, Russia 2AIRI Institute, Moscow, Russia. Correspondence to: Sergei Shumilin <s.shumilin@skoltech.ru>, Alexander Ryabov <a.ryabov@skoltech.ru>, Nikolay Yavich <n.yavich@skoltech.ru>, Evgeny Burnaev <e.burnaev@skoltech.ru>, Vladimir Vanovskiy <v.vanovskiy@skoltech.ru>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available on Git Hub1. 1https://github.com/Sergei Shumilin/Differentiable Unstructured Grid Coarsening |
| Open Datasets | No | The paper uses internally generated synthetic data described as "synthetic permeability generated by the following function: sin(ax) + sin(by) + 2.5 where a = b = 0.05." and refers to a "real oil reservoir model" in Appendix D without providing access details. |
| Dataset Splits | No | The paper mentions splitting 'train and test time periods' for simulation results, as shown by the 'Red dotted line splits training and test periods' in Fig. 11. However, it does not provide specific dataset splits (e.g., percentages or counts) for distinct training, validation, and test datasets in the conventional sense. |
| Hardware Specification | Yes | The experiments were conducted on Google Colab, utilizing two cores of an Intel(R) Xeon(R) CPU @ 2.20GHz and 12.7 GB of RAM, to ensure a consistent and replicable environment. |
| Software Dependencies | No | The paper mentions using "Py Torch (Paszke et al., 2017)" and "Pytorch Geometric (Fey & Lenssen, 2019) framework" but does not specify their version numbers. |
| Experiment Setup | Yes | For this experiment we ve done optimization for 20 epochs and modelled 104 time steps. Adam optimizer. Learning rate 10-3. |