Learning to Estimate and Refine Fluid Motion with Physical Dynamics
Authors: Mingrui Zhang, Jianhong Wang, James B Tlhomole, Matthew Piggott
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed approach outperforms optical flow methods and shows competitive results compared to existing supervised learning based methods on a benchmark dataset. Furthermore, the proposed approach can generalize to complex real-world fluid scenarios where ground truth information is effectively unknowable. Finally, experiments demonstrate that the physical corrector can refine flow estimates by mimicking the operator splitting method commonly utilised in fluid dynamical simulation. |
| Researcher Affiliation | Academia | 1Department of Earth Science and Engineering, Imperial College London, UK 2Department of Electrical and Electronic Engineering, Imperial College London, UK. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/erizmr/Learn-to-Estimate-Fluid-Motion. |
| Open Datasets | Yes | The PIV dataset is a synthetic dataset collected by (Cai et al., 2019b). |
| Dataset Splits | No | The predictor is trained on the PIV dataset with 12,190 samples used for training and 1505 for testing. A specific validation split size or methodology is not explicitly mentioned. |
| Hardware Specification | Yes | All experiments are conducted on a moderate level GPU Nvidia Tesla P100 16GB. |
| Software Dependencies | No | The paper mentions using the Adam optimiser but does not specify version numbers for any software libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | We train the model for 40,000 iterations with a batch size of four image pairs using the Adam optimiser. The learning rate is kept at 10 4. |