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..
Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks
Authors: Woojin Cho, Kookjin Lee, Donsub Rim, Noseong Park
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments We demonstrate that our proposed method significantly outperforms baselines on the 1-dimensional/2dimensional PDE benchmarks that are known to be very challenging for PINNs to learn [17, 18]. |
| Researcher Affiliation | Academia | Woojin Cho Kookjin Lee Donsub Rim Noseong Park Yonsei University Arizona State University Washington University in St. Louis EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper mentions 'See Appendix E for the formal algorithm' but Appendix E is not provided in the current document. No pseudocode or algorithm blocks are present in the main body. |
| Open Source Code | No | The paper refers to 'Appendix F, including hyperparameter configuration and software/hardware environments' for reproducibility, but does not provide an explicit statement about open-source code release or a link to a repository for the described methodology. |
| Open Datasets | No | The paper describes solving partial differential equations (PDEs) by training on generated collocation points, rather than using a publicly available dataset with specific access information. |
| Dataset Splits | No | The paper describes a two-phase training process (Phase 1 for meta-learning, Phase 2 for fine-tuning on test PDE parameters) and uses 'test collocation points' for evaluation, but does not specify distinct training, validation, and test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper mentions 'Appendix F, including hyperparameter configuration and software/hardware environments' for reproducibility, but Appendix F is not provided in the current document, and no specific hardware details are mentioned elsewhere. |
| Software Dependencies | No | The paper mentions 'Appendix F, including hyperparameter configuration and software/hardware environments' for reproducibility, but Appendix F is not provided in the current document, and no specific software dependencies with version numbers are mentioned elsewhere. |
| Experiment Setup | No | The paper refers to 'Appendix F, including hyperparameter configuration and software/hardware environments' for reproducibility, but Appendix F is not provided in the current document, and no specific experimental setup details or hyperparameter values are mentioned elsewhere. |