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].
Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions
Authors: Masanobu Horie, NAOTO MITSUME
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using numerical experiments, we demonstrate the proposed model s validity, expressibility, and computational efficiency. We use two types of datasets: 1) the gradient dataset to verify the correctness of NIso GCN and 2) the incompressible flow dataset to demonstrate the speed and accuracy of the model. |
| Researcher Affiliation | Collaboration | Masanobu Horie RICOS Co. Ltd. University of Tsukuba EMAIL Naoto Mitsume University of Tsukuba EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | The code is available at https://github.com/yellowshippo/penn-neurips2022. |
| Open Datasets | Yes | Our training, validation, and test datasets consisted of 100 samples. and Training, validation, and test datasets consisted of 203, 25, and 25 samples, respectively. |
| Dataset Splits | Yes | Our training, validation, and test datasets consisted of 100 samples. and Training, validation, and test datasets consisted of 203, 25, and 25 samples, respectively. |
| Hardware Specification | Yes | All models were trained for up to 24 hours using one GPU (NVIDIA A100 for NVLink 40Gi B HBM2). and All computation was done using one core of Intel Xeon CPU E5-2695 v2@2.40GHz. |
| Software Dependencies | No | The paper mentions 'Open FOAM' for dataset generation and states that 'The implementation of our model is based on the original Iso GCN s code,' but does not provide specific version numbers for software dependencies like Python, PyTorch, or other libraries used in the implementation. |
| Experiment Setup | Yes | We encoded each feature in a 4, 8, or 16-dimensional space. and We looped the solver for pressure five times and four or eight times for velocity. and We tested various time window sizes such as 2, 4, 10, and 20... The numbers of hidden features, 32, 64, and 128, were tested. All models were trained for up to 24 hours... |