以物理为导向的深度学习替代模型,以图表为准,用于在深层地质存储库中长期预测放射性核素运输
Dae Seong Jeong1, Jinuk Lee2, JongCheol Pyo3
1Future and Fusion Lab of Architectural, Civil and Environmental Engineering, Korea University, Seoul 02841, Republic of Korea.
Journal of hazardous materials
|December 17, 2025
概括
一个新的基于注意力的物理指导深度学习 (GAT-PGDL) 模型加速了深层地质存储库 (DGR) 的安全评估. 这种替代模型显著减少了模拟放射性废物运输的计算时间,使政策决策更快.
科学领域:
- 核工程与安全 核工程与安全
- 计算科学与工程 计算科学与工程
- 地质科学 地质科学
背景情况:
- 深层地质储存库 (DGR) 对于隔离高水平放射性废物至关重要.
- 目前的安全评估依赖于计算密集的基于过程的模拟器,如PFLOTRAN.
- 由于缺乏可行的长期监测,高计算负担阻碍了代的场景测试和及时的政策决策.
研究的目的:
- 为长期DGR安全评估开发一个计算效率高的替代模型.
- 将基本物理学,包括衰变-扩散-吸收方程,嵌入深度学习框架中.
- 为了支持政策,能够快速,可靠地模拟放射性核素运输.
主要方法:
- 开发了一个基于注意力的物理指导深度学习 (GAT-PGDL) 替代模型.
- 在DGR中模拟U-238和Th-230运输超过5000年.
- 雇员分离合规预测用于不确定性量化和敏感性分析用于可解释性.
主要成果:
- 与PFLOTRAN相比,GAT-PGDL实现了约61倍的加快速度,训练时间约94分钟,推断时间在几秒钟内.
- 该模型产生了95%的预测间隔,并确定了吸附和散装密度作为关键的运输控制.
- 在改变的场景下,GAT-PGDL在数据驱动的替代品上表现出优越的概括性能 (R2和NSE>0.98).
结论:
- GAT-PGDL为长期DGR安全评估提供了一个快速,准确和物理可靠的替代品.
- 开发的替代品可以克服计算瓶,促进代情景测试和明智的政策制定.
- 物理引导的深度学习为复杂的环境模拟提供了有希望的方法.
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