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..
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 |