Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Constants of motion network
Authors: Muhammad Firmansyah Kasim, Yi Heng Lim
NeurIPS 2022 | Venue PDF | 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 EMAIL |
| 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. |