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..
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 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on various SPDEs including the dynamic Ξ¦4 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 ΞΎ 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 Ξ± = (3, 1) in the feature sets. In the decoder layer, we use 4-layer 2d-FNO with s1 = 16, s2 = 16 and width = 8. |