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 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |