| dc.contributor.author |
Garg, Ayush |
|
| dc.contributor.author |
Kagi, Sammed Shantinath |
|
| dc.coverage.spatial |
France |
|
| dc.date.accessioned |
2022-03-26T10:11:11Z |
|
| dc.date.available |
2022-03-26T10:11:11Z |
|
| dc.date.issued |
2020-05 |
|
| dc.identifier.citation |
Garg, Ayush and Kagi, Sammed Shantinath, "[Re] hamiltonian neural networks", ReScience C, DOI: 10.5281/zenodo.3818621, May 2020. |
en_US |
| dc.identifier.issn |
2430-3658 |
|
| dc.identifier.uri |
https://doi.org/10.5281/zenodo.3818621 |
|
| dc.identifier.uri |
https://repository.iitgn.ac.in/handle/123456789/7602 |
|
| dc.description.abstract |
In today's world, neural networks are being in almost every discipline resulting in significant improvement in all the tools and applications. But in the field of Physics, they struggle to attain the basic laws like conservation of momentum. The paper Hamiltonian Neural Networks addresses this issue by using Hamiltonian mechanics to train the neural network in an unsupervised method. The following report is an explanation of the paper and the code to reproduce the claimed results. |
|
| dc.description.statementofresponsibility |
by Ayush Garg and Sammed Shantinath Kagi |
|
| dc.language.iso |
en_US |
en_US |
| dc.publisher |
Rescience |
en_US |
| dc.subject |
Hamiltonian neural networks |
en_US |
| dc.subject |
Momentum |
en_US |
| dc.subject |
Hamiltonian mechanics |
en_US |
| dc.subject |
Ideal pendulum |
en_US |
| dc.subject |
Two-body system |
en_US |
| dc.title |
[Re] hamiltonian neural networks |
en_US |
| dc.type |
Article |
en_US |
| dc.relation.journal |
ReScience |
|