Learnability of Linear Port-Hamiltonian Systems
Authors: Juan-Pablo Ortega, Daiying Yin
JMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The paper includes a section '7 Numerical illustrations' where it states: 'In this section, we present two numerical examples to demonstrate the effectiveness of our representation results from a learning point of view.' It also includes figures showing results and discussing training and testing data. |
| Researcher Affiliation | Academia | Both authors are affiliated with 'Nanyang Technological University, Singapore', which is an academic institution. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. It focuses on mathematical derivations and numerical examples. |
| Open Source Code | Yes | For the reader s convenience, the Python code necessary to reproduce these numerics is public and can be found in https: //github.com/YINDAIYING/Learnability-of-Linear-Port-Hamiltonian-Systems. |
| Open Datasets | No | The paper uses data generated from simulated systems ('Non-dissipative circuit' and 'Positive definite Frenkel-Kontorova model'). For example, in Section 7.1 it states: 'We randomly generate an initial condition for the ground-truth system and integrate it using Euler s method... The 1000 pairs of input and output data will be used as training data.' This indicates self-generated data, not a publicly available dataset with concrete access information. |
| Dataset Splits | Yes | In Section 7.1, it states: 'The 1000 pairs of input and output data will be used as training data. We set a testing period of 4000 time steps'. In Section 7.2, it states: 'The 1000 pairs of input and output data are then used as training data.' |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. It only mentions 'Python code' in relation to the numerical illustrations. |
| Software Dependencies | No | The paper mentions 'Python code' but does not specify any software names with version numbers (e.g., Python version, specific libraries like NumPy, SciPy, or PyTorch versions). |
| Experiment Setup | Yes | In Section 7.1, it states: 'This is carried out via gradient descent using a learning rate of λ = 0.1 for 500 epochs.' In Section 7.2, it states: 'we carry out the training using gradient descent with a learning rate of λ = 0.02 over 1500 epochs out of randomly chosen initial values for the initial state condition and the model parameters'. |