EGU25-11123, updated on 15 Mar 2025
https://doi.org/10.5194/egusphere-egu25-11123
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
Physics-Informed Neural Networks for Hydraulic Monitoring in Water Diversion Projects with Limited Cross-Section Data
- 1Hohai University, College of Agricultural Science and Engineering, Nanjing, China (jgfeng@hhu.edu.cn)
- 2Hohai University, College of Computer Science and Software Engineering, Nanjing, China
- 3University of Alberta, Department of Civil and Environmental Engineering, Canada
How to cite: Feng, J., Li, Z., Mu, T., Li, X., Li, P., and Qian, S.: Physics-Informed Neural Networks for Hydraulic Monitoring in Water Diversion Projects with Limited Cross-Section Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11123, https://doi.org/10.5194/egusphere-egu25-11123, 2025.