Zhong, Ming
Yan, Zhenya
Tian, Shou-Fu
Funding for this research was provided by:
National Natural Science Foundation of China (11731014)
Article Title: Data-driven parametric soliton-rogon state transitions for nonlinear wave equations using deep learning with Fourier neural operator
Journal Title: Communications in Theoretical Physics
Article Type: paper
Copyright Information: © 2023 Institute of Theoretical Physics CAS, Chinese Physical Society and IOP Publishing. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Publication dates
Date Received: 2022-07-28
Date Accepted: 2022-12-14
Online publication date: 2023-02-06