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].
PAPM: A Physics-aware Proxy Model for Process Systems
Authors: Pengwei Liu, Zhongkai Hao, Xingyu Ren, Hangjie Yuan, Jiayang Ren, Dong Ni
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through systematic comparisons with state-of-the-art pure datadriven and physics-aware models across five twodimensional benchmarks in nine generalization tasks, PAPM notably achieves an average performance improvement of 6.7%, while requiring fewer FLOPs, and just 1% of the parameters compared to the prior leading method. |
| Researcher Affiliation | Academia | 1Zhejiang University, Hangzhou, Zhejiang, China 2Tsinghua University, Beijing, China 3University of British Columbia, Vancouver, BC, Canada. |
| Pseudocode | Yes | The Appendix D.1 provides the pseudo-code for the entire training process, offering a comprehensive understanding of our approach. As shown in Alg. 1, the structure-preserved localized operator is detailed. The latter is shown in Alg. 2, and the third one, the hybrid operator, is a combination of these two operators. |
| Open Source Code | Yes | Code is available at https://github.com/pengwei07/PAPM. |
| Open Datasets | Yes | RD2d (Takamoto et al., 2022) ... This dataset can be downloaded at https://github.com/pdebench/PDEBench |
| Dataset Splits | Yes | For C Int., the data is uniformly shuffled and then split into training, validation, and testing datasets in a [7 : 1 : 2] ratio. |
| Hardware Specification | Yes | all experiments are run on 1 3 NVIDIA Tesla P100 GPUs. |
| Software Dependencies | No | The paper mentions training models with "Adam W (Loshchilov & Hutter, 2017) optimizer" but does not specify software versions for programming languages, libraries, or frameworks (e.g., Python version, PyTorch/TensorFlow version). |
| Experiment Setup | Yes | We train all models with Adam W (Loshchilov & Hutter, 2017) optimizer with the exponential decaying strategy, and epochs are set as 500. The causality parameter α1 = 0.1 and α0 = 0.001. The initial learning rate is 1e-3, and the Reduce LRon Plateau schedule is utilized with a patience of 20 epochs and a decay factor of 0.8. For a fair comparison, the batch size is identical across all methods for the same task |