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 | Conference PDF | Archive PDF | Plain Text | 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 han.shao@kaust.edu.sa Tassilo Kugelstadt RWTH Aachen University kugelstadt@cs.rwth-aachen.de Torsten H adrich KAUST torsten.hadrich@kaust.edu.sa Wojciech Pałubicki AMU wp06@amu.edu.pl Jan Bender RWTH Aachen University bender@cs.rwth-aachen.de S oren Pirk Google Research pirk@google.com Dominik L. Michels KAUST dominik.michels@kaust.edu.sa |
| 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. |