Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
DATS: Difficulty-Aware Task Sampler for Meta-Learning Physics-Informed Neural Networks
Authors: Maryam Toloubidokhti, Yubo Ye, Ryan Missel, Xiajun Jiang, Nilesh Kumar, Ruby Shrestha, Linwei Wang
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 23,282 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,849 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated DATS against uniform and self-paced task-sampling baselines on two representative meta-PINN models, across five benchmark PDEs as well as three different residual point sampling strategies. The results demonstrated that DATS was able to improve the accuracy of meta-learned PINN solutions when reducing performance disparity across PDE configurations, at only a fraction of residual sampling budgets required by its baselines. |
| Researcher Affiliation | Academia | 1Rochester Institute of Technology, Rochester, NY, USA 2Zhejiang University, Hangzhou, China {mt6129}@rit.edu, {22230131}@zju.edu.cn |
| Pseudocode | No | The paper describes algorithms and derivations but does not include explicit pseudocode blocks or algorithm boxes. |
| Open Source Code | Yes | Source code available at https://github.com/maryamTolou/DATS_ICLR2024. |
| Open Datasets | No | The paper uses benchmark PDE equations and defines training and test configurations for these PDEs, but it does not provide a direct link or specific access information for a static, publicly available dataset file. |
| Dataset Splits | Yes | Table 2: The range and number of PDE configurations considered in each PDE benchmark. PDE Configuration #Training #Test... Burger ... 14 6... Convection ... 5 3... Reaction Diffusion ... 9 4... Helmholtz (2D) ... 9 4. |
| Hardware Specification | Yes | Experiments were run on NVIDIA Tesla T4s with 16 GB memory. |
| Software Dependencies | No | The paper mentions optimizers like ADAM and general network architectures, but it does not specify software versions (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Appendix B.1 BURGERS EQUATION: Fully Connected Layers Number of Layers: 7 Hidden layers dimenstion: 8 ... Optimizer: ADAM Learning rate: 1e-4 with cosine annealing Epochs: 20000. |