Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps
Authors: Renyi Chen, Molei Tao
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments further demonstrate our claims. |
| Researcher Affiliation | Academia | 1School of Mathematics, Georgia Institute of Technology, Atlanta, GA, USA. |
| Pseudocode | Yes | Algorithm 1 GFNN |
| Open Source Code | No | The paper does not provide any explicit statements about making its own source code available for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | No | The paper describes generating data from various Hamiltonian systems (e.g., '2D Keplerian orbit', 'H enon-Heiles system', 'PCR3BP') for its experiments, but it does not provide concrete access information (link, DOI, repository, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions using 'training data' and that 'Details of data preparation and training are in appendix B.', but it does not specify exact percentages or sample counts for training, validation, or test splits in the provided text. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any ancillary software or libraries used in the experiments. |
| Experiment Setup | No | The paper states 'Details of data preparation and training are in appendix B.', indicating that experimental setup details exist, but these details (e.g., hyperparameters, optimizer settings) are not provided within the main text of the paper. |