Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics

A. N. Kolmogorov, On the representation of continuous functions of several variables by superpositions of continuous functions of a smaller number of variables . American Mathematical Society, 1961

1961

Earlier work this paper cites.

T. Chen and H. Chen, “Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems,” IEEE transactions on neural networks , vol. 6, no. 4, pp. 911–917, 1995

1995

Earlier work this paper cites.

H. Jiang, J. J. Zhang, W. Gao, and Z. Wu, “Fault detection, identification, and location in smart grid based on data-driven computational methods,” IEEE Transactions on Smart Grid , vol. 5, no. 6, pp. 2947–2956, 2014

2014

Earlier work this paper cites.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, pp. 436–444, 2015

2015

Earlier work this paper cites.

M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations,” arXiv preprint arXiv:1711.10561 , 2017

Original

2017

Earlier work this paper cites.

M. I. Razzak, S. Naz, and A. Zaib, “Deep learning for medical image processing: Overview, challenges and the future,” Classification in BioApps: Automation of decision making , pp. 323–350, 2018

2018

Earlier work this paper cites.

R. Kemker, M. McClure, A. Abitino, T. Hayes, and C. Kanan, “Measuring catastrophic forgetting in neural networks,” in Proceedings of the AAAI conference on artificial intelligence , vol. 32, no. 1, 2018

2018

Earlier work this paper cites.

V. Vittal, J. D. McCalley, P. M. Anderson, and A. Fouad, Power System Control and Stability, 3rd Edition . John Wiley & Sons, 2019

2019

Earlier work this paper cites.

T. Hong, P. Pinson, Y. Wang, R. Weron, D. Yang, and H. Zareipour, “Energy forecasting: A review and outlook,” IEEE Open Access Journal of Power and Energy , vol. 7, pp. 376–388, 2020

2020

Earlier work this paper cites.

S. Sinha, S. P. Nandanoori, and E. Yeung, “Data driven online learning of power system dynamics,” in 2020 IEEE Power & Energy Society General Meeting (PESGM) , 2020, pp. 1–5

2020

Cited alongside, same era.

G. S. Misyris, A. Venzke, and S. Chatzivasileiadis, “Physics-informed neural networks for power systems,” in 2020 IEEE power & energy society general meeting (PESGM) . IEEE, 2020, pp. 1–5

2020

Cited alongside, same era.

Y. Lu and J. Lu, “A universal approximation theorem of deep neural networks for expressing probability distributions,” Advances in neural information processing systems , vol. 33, pp. 3094–3105, 2020

2020

Cited alongside, same era.

T. Poggio, A. Banburski, and Q. Liao, “Theoretical issues in deep networks,” Proceedings of the National Academy of Sciences , vol. 117, no. 48, pp. 30 039–30 045, 2020

2020

Cited alongside, same era.

R. Satheesh, N. Chakkungal, S. Rajan, M. Madhavan, and H. H. Alhelou, “Identification of oscillatory modes in power system using deep learning approach,” IEEE Access , vol. 10, pp. 16 556–16 565, 2022

2022

Later among the works it cites.

Y. Zhang, X. Shi, H. Zhang, Y. Cao, and V. Terzija, “Review on deep learning applications in frequency analysis and control of modern power system,” International Journal of Electrical Power & Energy Systems , vol. 136, p. 107744, 2022

2022

Later among the works it cites.

Z. Liu, Y. Wang, S. Vaidya, F. Ruehle, J. Halverson, M. Soljačić, T. Y. Hou, and M. Tegmark, “Kan: Kolmogorov-arnold networks,” arXiv preprint arXiv:2404.19756 , 2024

Original

2024

Closest in time.

H. Shuai, B. She, J. Wang, and F. Li, “Safe reinforcement learning for grid-forming inverter based frequency regulation with stability guarantee,” Journal of Modern Power Systems and Clean Energy , pp. 1–8, 2024

2024

Closest in time.