Peer-reviewedCC BY-NC-ND 4.0Nature Communications
Discovering network resilience formulas with symbolized reinforcement learning
Related
- Discovering like a scientist: Literature-informed dimensionless learning for physical closure of data-driven equations
- Reinforcement learning for robust calibration of multiqudit quantum gates
- Reinforcement learning to disentangle multiqubit quantum states from partial observations
- On physical implementability of reinforcement-learning-driven reasoning
- Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits
BibTeX
@article{zheng2026discovering,
title = {{Discovering network resilience formulas with symbolized reinforcement learning}},
author = {Yu Zheng and Jingtao Ding and Depeng Jin and Jianxi Gao and Yong Li},
journal = {Nature Communications},
year = {2026},
month = oct,
doi = {10.1038/s41467-026-78269-3},
url = {https://www.nature.com/articles/s41467-026-78269-3},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Discovering network resilience formulas with symbolized reinforcement learning
AU - Yu Zheng
AU - Jingtao Ding
AU - Depeng Jin
AU - Jianxi Gao
AU - Yong Li
PY - 2026
DA - 2026/10/19
JO - Nature Communications
DO - 10.1038/s41467-026-78269-3
UR - https://www.nature.com/articles/s41467-026-78269-3
SN - 2041-1723
ER -