Meta-Learning Dynamics Forecasting Using Task Inference
Authors: Rui Wang, Robin Walters, Rose Yu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we demonstrate that our model outperforms state-of-the-art approaches to forecasting complex physical dynamics including turbulent flow, real-world sea surface temperature and ocean currents. |
| Researcher Affiliation | Academia | Rui Wang* UC San Diego Robin Walters* Northeastern University Rose Yu UC San Diego |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available open sourced at https://github.com/Rose-STL-Lab/Dynamic-Adaptation-Network. |
| Open Datasets | Yes | We generate a synthetic dataset of turbulent flows with a numerical simulator, Phi Flow1. 1https://github.com/tum-pbs/Phi Flow. We evaluate on a real-world sea surface temperature data generated by the NEMO ocean engine [38]2. 2The data are available at https://resources.marine.copernicus.eu/?option=com_csw&view= details&product_id=GLOBAL_ANALYSIS_FORECAST_PHY_001_024 |
| Dataset Splits | Yes | For test-future, we train and test on the same task but different time steps. For test-domain, we train and test on different tasks with an 80-20 split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'Phi Flow' and 'NEMO ocean engine' but does not specify their version numbers or other software dependencies with versions. |
| Experiment Setup | No | The paper describes general training strategies like 'multi-step ahead predictions' and 'averaged over 3 runs with random initialization', but does not provide specific hyperparameters such as learning rates, batch sizes, or exact epoch counts for the DyAd model itself in the main text. |