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 [1].
Weak Form Generalized Hamiltonian Learning
Authors: Kevin Course, Trefor Evans, Prasanth Nair
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Numerical Studies We compare our approach (GHNN) to a fully connected neural network (FCNN) and Hamiltonian neural network (HNN). All models were trained on an Nvidia Ge Force GTX 980 Ti GPU. ... The error in the states, its derivatives, and training time for the three methods of parameter estimation are given in Table 1. |
| Researcher Affiliation | Academia | Kevin L. Course University of Toronto EMAIL Trefor W. Evans University of Toronto EMAIL Prasanth B. Nair University of Toronto EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code can be found online at: https://github.com/coursekevin/weakformghnn. |
| Open Datasets | No | The paper generates synthetic data by collecting 'measurements of the pendulum state corrupted by Gaussian noise' or 'measurements of the state corrupted by Gaussian noise' from well-known dynamical systems (nonlinear pendulum, Lorenz 63, Duffing oscillator) rather than using pre-existing public datasets with explicit access information. |
| Dataset Splits | No | The paper describes collecting measurements for training and evaluation but does not specify explicit train/validation/test dataset splits (e.g., percentages, sample counts, or predefined split references). |
| Hardware Specification | Yes | All models were trained on an Nvidia Ge Force GTX 980 Ti GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'torchdiffeq' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Unless otherwise noted, we will use the default settings for the adjoint ODE solvers offered in Chen et al. s package; at the time of writing, this includes a relative tolerance of 10 6 and an absolute tolerance of 10 12 with a Runge-Kutta(4)5 adaptive ODE solver. ... For all experiments that use weak derivative regression, the test space is spanned by 200 evenly spaced Gaussian radial basis functions with a shape parameter of 10 over each mini-batch integration window; |