CNS_Papers

AI Paper

• Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." nature 518.7540 (2015): 529-533. [Paper]

AI4Science

• Li Z, Han W, Zhang Y, et al. Learning spatiotemporal dynamics with a pretrained generative model[J]. Nature Machine Intelligence, 2024, 6(12): 1566-1579. [Paper] [Nature]

Medical

• AI-based diagnosis of acute aortic syndrome from noncontrast CT. Hu, Y., Xiang, Y., Zhou, Y. J., He, Y., Lang, D., Yang, S., ... & Zhang, H. (2025). Nature Medicine, 1-13. [Paper]

• "A generative model uses healthy and diseased image pairs for pixel-level chest X-ray pathology localization." Dong, Kaiming, Yuxiao Cheng, Kunlun He, and Jinli Suo. Nature Biomedical Engineering (2025): 1-13. [Paper]

• Exploring scalable medical image encoders beyond text supervision, Fernando Pérez-García et al. [Paper] [Code]

Event/Spike Data

• Gehrig, D., & Scaramuzza, D. (2024). Low-latency automotive vision with event cameras. Nature, 629(8014), 1034-1040. [Paper] [Code]

Fusion

• Tokuzawa, Tokihiko, et al. "Cross-scale nonlinear interaction and bifurcation in multi-scale turbulence of high-temperature plasmas." Communications Physics 8.1 (2025): 394. [Paper] [微信公众号]

• Wang, Allen M., et al. "Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV." Nature Communications 16.1 (2025): 8877. [Paper]

• Degrave J, Felici F, Buchli J, et al. Magnetic control of tokamak plasmas through deep reinforcement learning[J]. Nature, 2022, 602(7897): 414-419. [Paper]

• Seo J, Kim S K, Jalalvand A, et al. Avoiding fusion plasma tearing instability with deep reinforcement learning[J]. Nature, 2024, 626(8000): 746-751. [Paper]

• Kates-Harbeck, Julian, Alexey Svyatkovskiy, and William Tang. "Predicting disruptive instabilities in controlled fusion plasmas through deep learning." Nature 568.7753 (2019): 526-531. [Paper]

• Ding S, Garofalo A M, Wang H Q, et al. A high-density and high-confinement tokamak plasma regime for fusion energy[J]. Nature, 2024, 629(8012): 555-560. [Paper]

• Kim S K, Shousha R, Yang S M, et al. Highest fusion performance without harmful edge energy bursts in tokamak[J]. Nature communications, 2024, 15(1): 3990. [Paper]

• Murari A, Rossi R, Craciunescu T, et al. A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors[J]. Nature Communications, 2024, 15(1): 2424. [Paper]

• Yang S M, Park J K, Jeon Y M, et al. Tailoring tokamak error fields to control plasma instabilities and transport[J]. Nature Communications, 2024, 15(1): 1275. [Paper]

• Dominguez-Palacios J, Futatani S, Garcia-Munoz M, et al. Effect of energetic ions on edge-localized modes in tokamak plasmas[J]. Nature Physics, 2025, 21(1): 43-51. [Paper]

• Jenko, Frank. "Accelerating fusion research via supercomputing." Nature Reviews Physics (2025): 1-13. [Paper]

• Garcia J, Kazakov Y, Coelho R, et al. Stable Deuterium-Tritium plasmas with improved confinement in the presence of energetic-ion instabilities[J]. Nature Communications, 2024, 15(1): 7846. [Paper]

• Gibney E. ITER delay: what it means for nuclear fusion[J]. Nature, 2024, 631(8021): 488-489. [Paper]

• Conroy G. CHINA’S RACE FOR FUSION ENERGY[J]. Nature, 2024, 632(8027): 968-970. [Paper]

• Ding S, Garofalo A M, Wang H Q, et al. Author Correction: A high-density and high-confinement tokamak plasma regime for fusion energy[J]. Nature, 2024, 630(8016): E4. [Paper]

• Willensdorfer, Matthias, et al. "Observation of magnetic islands in tokamak plasmas during the suppression of edge-localized modes." Nature Physics 20.12 (2024): 1980-1988. [Paper]

• Chouchene, Sarah, et al. "Application of machine learning for detecting and tracking turbulent structures in plasma fusion devices using ultra fast imaging." Scientific Reports 14.1 (2024): 27965. [Paper]

• Li, Wenyang, et al. "Excited ion-scale turbulence by a magnetic island in fusion plasmas." Scientific Reports 14.1 (2024): 25362. [Paper]

• Höfler, Klara, et al. "Milestone in predicting core plasma turbulence: successful multi-channel validation of the gyrokinetic code GENE." Nature communications 16.1 (2025): 2558. [Paper]

• Geng J S, Li P, Li Y D, et al. Spontaneous evolution of density peaking factor in TEM turbulence-dominated H-mode plasma on the EAST Tokamak[J]. Scientific Reports, 2025, 15(1): 7738. [Paper]

• Yang C, Li K, Li G, et al. Prediction of the kinetic profiles in H-mode plasma discharges on EAST using core-pedestal coupling[J]. Scientific Reports, 2025, 15(1): 9207. [Paper]