Work

Physics-guided deep learning surrogate model with graph attention for long-term radionuclide transport prediction in deep geological repositories

Year: 2025

Type: article

Source: Journal of Hazardous Materials

DOI: 10.1016/j.jhazmat.2025.140767

Authors: Dae Seong Jeong, Jinuk Lee, JongCheol Pyo, Sang-Soo Baek, Jin Hwi Kim, Mi-Seon Jeong, Hyungju Yun, Kyung Hwa Cho

Institutions: Korea University, Ulsan National Institute of Science and Technology, Pusan National University, Yeungnam University, Korea Radioactive Waste Agency, Chulalongkorn University

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: closed

Funders: Institute for Korea Spent Nuclear Fuel, Ministry of Trade, Industry and Energy, Korea Institute of Energy Technology Evaluation and Planning

Award: G2019944739

OpenAlex ID: W4417166319

FWCI: 1.144

Cites: 47

Cited by: 4

Related to: 10