Deep neural networks as discrete dynamical systems: Implications for physics-informed learning
- DOI
- 10.1063/5.0315971
- Published
- 2026-07-01
- Container
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1063/5.0315971,
title = {Deep neural networks as discrete dynamical systems: Implications for physics-informed learning},
author = {Abhisek Ganguly and Santosh Ansumali and Sauro Succi},
year = {2026},
journal = {The Journal of Chemical Physics},
doi = {10.1063/5.0315971},
url = {https://doi.org/10.1063/5.0315971}
}RIS
TY - JOUR TI - Deep neural networks as discrete dynamical systems: Implications for physics-informed learning AU - Abhisek Ganguly AU - Santosh Ansumali AU - Sauro Succi PY - 2026 JO - The Journal of Chemical Physics DO - 10.1063/5.0315971 UR - https://doi.org/10.1063/5.0315971 ER -
APA
Ganguly, A., Ansumali, S., & Succi, S. (2026). Deep neural networks as discrete dynamical systems: Implications for physics-informed learning. The Journal of Chemical Physics. https://doi.org/10.1063/5.0315971
Source records
- crossref · retrieved 2026-09-25T06:05:46.050Z