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..
Accurately Solving Rod Dynamics with Graph Learning
Authors: Han Shao, Tassilo Kugelstadt, Torsten Hädrich, Wojtek Palubicki, Jan Bender, Soeren Pirk, Dominik L Michels
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We generate training and validation datasets based on two scenarios: an initially straight bending rod and an elastic helix each fixed at one end oscillating under the influence of gravity. The specification of these datasets is provided in Table 1. |
| Researcher Affiliation | Collaboration | KAUST EMAIL Tassilo Kugelstadt RWTH Aachen University EMAIL Torsten H adrich KAUST EMAIL Wojciech Pałubicki AMU EMAIL Jan Bender RWTH Aachen University EMAIL S oren Pirk Google Research EMAIL Dominik L. Michels KAUST EMAIL |
| Pseudocode | Yes | Algorithm 1 Numerical integration procedure updating pi,t 7! pi,t+ t and υi,t 7! υi,t+ t. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] This can be found in the supplement material. |
| Open Datasets | No | The paper states: 'We generate training and validation datasets based on two scenarios: an initially straight bending rod and an elastic helix each fixed at one end oscillating under the influence of gravity.' While it mentions data is in supplementary material, it doesn't provide a specific link, DOI, or formal citation for this generated dataset to be considered publicly available in the sense of the question. |
| Dataset Splits | Yes | Table 1: Specification of training and validation datasets for the two scenarios of an initially straight bending rod (top) and an elastic helix (bottom). The datasets are comprised of a number of data points (left) each describing the rod s dynamics within t 2 [0 s, 50 t] discretized with a time step size of t = 0.02 s. |
| Hardware Specification | Yes | The training is performed on an NVIDIA Tesla V100 GPU. |
| Software Dependencies | No | The paper states: 'The PBD code is written in C++, while the COPINGNet is implemented in Py Torch.' No specific version numbers for PyTorch or other libraries are provided. |
| Experiment Setup | Yes | A constant learning rate of = 0.001 was used and a mean square loss function was employed. |