DiffPhyCon: A Generative Approach to Control Complex Physical Systems
Authors: Long Wei, Peiyan Hu, Ruiqi Feng, Haodong Feng, Yixuan Du, Tao Zhang, Rui Wang, Yue Wang, Zhi-Ming Ma, Tailin Wu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method on three tasks: 1D Burgers equation, 2D jellyfish movement control, and 2D highdimensional smoke control, where our generated jellyfish dataset is released as a benchmark for complex physical system control research. Our method outperforms widely applied classical approaches and state-of-the-art deep learning and reinforcement learning methods. |
| Researcher Affiliation | Collaboration | 1School of Engineering, Westlake University, 2Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 3Jilin University, 4Fudan University, 5Microsoft AI4Science |
| Pseudocode | Yes | Algorithm 1 Inference for Diff Phy Con |
| Open Source Code | Yes | The project website, jellyfish dataset, and code can be found at https://github.com/AI4Science-Westlake U/diffphycon. |
| Open Datasets | Yes | our generated jellyfish dataset is released as a benchmark for complex physical system control research. |
| Dataset Splits | No | The paper specifies 'training set' and 'testing set' sizes for all experiments, but does not explicitly mention or detail a 'validation set' or its use for hyperparameter tuning. |
| Hardware Specification | Yes | The training is performed on two NVIDIA Tesla A100 GPUs with 80 GB memory for about 3 days. ... The training is performed on two NVIDIA Tesla A6000 GPUs with 48 GB memory for about 2 days. ... Inference time is tested on a Tesla-V100 GPU with 8 CPUs. |
| Software Dependencies | No | The paper mentions software tools like 'Lily-Pad simulator' and 'Phiflow solver' and optimizers like 'Adam' but does not provide specific version numbers for these or any other software dependencies, such as deep learning frameworks. |
| Experiment Setup | Yes | Table 5: Hyperparameters of the UNet architecture and training for the results of 1D Burgers equation in Table 1. |