Deep Latent Regularity Network for Modeling Stochastic Partial Differential Equations
Authors: Shiqi Gong, Peiyan Hu, Qi Meng, Yue Wang, Rongchan Zhu, Bingguang Chen, Zhiming Ma, Hao Ni, Tie-Yan Liu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on various SPDEs including the dynamic Φ4 1 model and the stochastic 2D Navier-Stokes equation to predict their solutions, and the results demonstrate that the proposed DLR-Net can achieve SOTA accuracy compared with the baselines. |
| Researcher Affiliation | Collaboration | 1 Academy of Mathematics and Systems Science, Chinese Academy of Sciences 2 Microsoft Research AI4Science 3 Bielefeld University 4 Department of Mathematics, University College London 5 The Alan Turing Institute |
| Pseudocode | Yes | Algorithm 1: Generation of Model Feature Vectors |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | Space-time white noise ξ and reference solution u on this grid are given by numerical simulator in (Chevyrev, Gerasimovics, and Weber 2021) on this grid. |
| Dataset Splits | No | The paper mentions 'training data size N = 1000 or 10000' and training and testing models, but does not provide specific train/validation/test split percentages or sample counts for each split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as library or solver names. |
| Experiment Setup | Yes | For this equation, we use two RF blocks with height n = 2 and α = (3, 1) in the feature sets. In the decoder layer, we use 4-layer 2d-FNO with s1 = 16, s2 = 16 and width = 8. |