Constants of motion network
Authors: Muhammad Firmansyah Kasim, Yi Heng Lim
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our implementation and experiments can be found in the public domain1. [...] To demonstrate the capability of COMET to simultaneously learn both the dynamics and the constants of motion, we tested it in a variety of cases. [...] For each case, we compared the performance of COMET with other methods: (1) simple neural ODE (NODE) [10], (2) Hamiltonian neural network (HNN) [6] with the coordinates given in each case below, (3) neural symplectic form (NSF) [7], and (4) Lagrangian neural network (LNN) [8]. |
| Researcher Affiliation | Industry | M. F. Kasim & Y. H. Lim Machine Discovery Ltd. Oxford, United Kingdom {muhammad, yi.heng}@machine-discovery.com |
| Pseudocode | No | The paper describes the computational procedures mathematically and in prose, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation and experiments can be found in the public domain1. 1https://github.com/machine-discovery/comet/ |
| Open Datasets | No | For all the cases in this section, the training data were generated by simulating the dynamics of the system from t = 0 to t = 10. From the simulations, we collected the states s as well as the states rate of change, ˆ s, which were calculated analytically and were added a Gaussian noise with standard deviation σ = 0.05. |
| Dataset Splits | No | The paper describes the generation of training and test data, but it does not specify a validation dataset split or a validation phase in the experimental setup. |
| Hardware Specification | Yes | The training was done as described in section 4 which takes about 5-7 hours on an NVIDIA T4 GPU. |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | In order to train COMET, the loss function in this case is constructed as L = s ˆ s 2 + w1 s0 ˆ s 2 + w2 i=1 ci s0 2 , where w are the tunable regularization weights. [...] The neural network architecture for each method is detailed in appendix ??. [...] Specifically, COMET was trained in the damped pendulum, two body, and 2D nonlinear spring cases from section 4 without added noise and ran for 3000 epochs. [...] The neural network was constructed with 1D convolutional layers with kernel size 5 and circular padding, followed by logsigmoid activation function. The pattern above was repeated 4 times but without the activation function for the last one, using 250 channels in the hidden layers. |