Sparse Symplectically Integrated Neural Networks
Authors: Daniel DiPietro, Shiying Xiong, Bo Zhu
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate SSINNs on four classical Hamiltonian dynamical problems: the Hénon-Heiles system, nonlinearly coupled oscillators, a multi-particle mass-spring system, and a pendulum system. Our results demonstrate promise in both system prediction and conservation of energy, often outperforming the current state-of-the-art black-box prediction techniques by an order of magnitude. |
| Researcher Affiliation | Academia | Daniel M. Di Pietro, Shiying Xiong, Bo Zhu Dartmouth College, Department of Computer Science {daniel.m.dipietro.22, shiying.xiong, bo.zhu}@dartmouth.edu |
| Pseudocode | No | The paper does not contain a clearly labeled section for pseudocode or algorithm blocks. Figure 1 illustrates the computational flow but is not presented as pseudocode. |
| Open Source Code | Yes | 1Our code is available at https://github.com/dandip/ssinn |
| Open Datasets | No | The paper describes generating custom datasets for its experiments (e.g., 'The Hénon-Heiles dataset consists of 5,000 points... We computed this dataset via Clean Numerical Simulation', 'we first generated a clean dataset of 500 randomly sampled state transitions'). It does not provide access information (links, DOIs, formal citations to publicly available versions) for these datasets. |
| Dataset Splits | Yes | All SSINNs converged to the governing Hamiltonian on the clean dataset, with the best-performing model achieving prediction error of 2 10 8 for computing t = 0 to t = 0.1 on a validation dataset of 100 points. |
| Hardware Specification | Yes | trained using ADAM for 5 epochs on an RTX 2080 Ti system [18]. |
| Software Dependencies | No | The paper mentions using 'ADAM' as an optimizer but does not specify versions for any other software components, libraries, or programming languages (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | Each model had an initial learning rate of 10 3 with decay and was trained using ADAM for 5 epochs... A regularization coefficient of 10 3 led to the best results. and we increased the initial learning rate to 10 2 and trained for 60 epochs... Additionally, the regularization coefficient was tuned to 8 10 3. and altered the regularization parameter to 4 10 4. Similarly, we increased the SRNN models to 1024 hidden nodes per layer. All models were trained for 30 epochs with an initial learning rate of 10 2. |