Learning Physical Dynamics with Subequivariant Graph Neural Networks
Authors: Jiaqi Han, Wenbing Huang, Hengbo Ma, Jiachen Li, Josh Tenenbaum, Chuang Gan
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct evaluations on Physion [4] and Rigid Fall [21]. Experimental results show that our model is capable of yielding more accurate dynamics prediction, is highly data-efficient, and has strong generalization compared with the state-of-the-art learning-based differentiable physical simulators. |
| Researcher Affiliation | Collaboration | Jiaqi Han Tsinghua University Wenbing Huang Gaoling School of Artificial Intelligence, Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Hengbo Ma University of California, Berkeley Jiachen Li Stanford University Joshua B. Tenenbaum MIT BCS, CBMM, CSAIL Chuang Gan UMass Amherst MIT-IBM Watson AI Lab |
| Pseudocode | No | The paper describes the methodology using equations and textual descriptions, but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Code and videos are available at our project page: https://hanjq17.github.io/SGNN/. |
| Open Datasets | Yes | We conduct evaluations on Physion [4] and Rigid Fall [21]. |
| Dataset Splits | Yes | The networks are trained with an Adam optimizer, using an initial learning rate 0.0001 and an early-stopping of 10 epochs on the validation loss. |
| Hardware Specification | No | The paper does not explicitly state the specific hardware used for running the experiments, such as GPU models or CPU specifications. |
| Software Dependencies | No | The paper mentions using an 'Adam optimizer' and 'MLPs' but does not provide specific version numbers for software dependencies such as deep learning frameworks or libraries. |
| Experiment Setup | Yes | In detail, all MLPs are initialized with 3 projection layers and a hidden dimension of 200. The networks are trained with an Adam optimizer, using an initial learning rate 0.0001 and an early-stopping of 10 epochs on the validation loss. We use 4 iterations in each message passing of our model. |