Symplectic Neural Gaussian Processes for Meta-learning Hamiltonian Dynamics
Authors: Tomoharu Iwata, Yusuke Tanaka
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we demonstrate that the proposed method outperforms existing methods for predicting dynamics from a small number of observations in target systems. |
| Researcher Affiliation | Industry | Tomoharu Iwata , Yusuke Tanaka NTT Corporation {tomoharu.iwata,ysk.tanaka}@ntt.com |
| Pseudocode | Yes | Algorithm 1 Meta-learning procedure of our SNGP model. |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of its source code. |
| Open Datasets | No | The paper describes generating data from six types of dynamical systems (mass-spring, pendulum, Duffing with and without friction) with randomly determined physical parameters and initial conditions, rather than using a pre-existing publicly available dataset with a specific link or citation. |
| Dataset Splits | Yes | For each type, five systems were used for meta-training, three for metavalidation, and six for meta-test. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'functorch' but does not specify their version numbers. |
| Experiment Setup | Yes | For obtaining system representation in Eq. (3), we used the bidirectional LSTM [Graves and Graves, 2012] for RNN with 32 hidden units, where the sequence of the states was used for input. For NNz and NNk, we used three-layered feedforward neural networks with 32 hidden and output units. For NNm, we used four-layered feed-forward neural networks with 32 hidden units. For the activation function, we used the hyperbolic tangent. We optimized our models using Adam [Kingma and Ba, 2015] with learning rate 10^-3, and batch dataset size four. The meta-validation datasets were used for early stopping, for which the maximum number of meta-training epochs was 5,000. |