南京大学学报（自然科学版）2024，Vol.60Issue(2)：317-327,11.DOI:10.13232/j.cnki.jnju.2024.02.012
基于PINNs算法的一维潜水流方程的渗流参数反演
Inversion of seepage parameters for one-dimensional unconfined aquifer flow equations based on PINNs algorithm
摘要
Abstract
In the field of groundwater,the inversion of seepage parameters contributes to understanding the nature of groundwater flow,which is essential for determining the distribution,movement,and quality of groundwater resources.This plays a significant role in groundwater resource management,hydrological model development,and the sustainability of groundwater recharge.Despite the rapid development of neural network methods in recent years,there is limited research focused on the inversion of seepage parameters for unconfined flow.Addressing this,the present study pioneers the application of Physics-Informed Neural Networks(PINNs)combined with both soft and hard constraints to solve the problem of permeability coefficient inversion in unconfined aquifers.Taking the permeability coefficient inversion in one-dimensional steady-state heterogeneous unconfined flow as well as unsteady-state homogeneous unconfined flow(including solute transport)as examples,this paper compares the performance of the PINNs soft constraint method(PINNs-S)and hard constraint method(PINNs-H)in inverting permeability coefficients.The PINNs case studies indicate that the PINNs algorithm inverts permeability coefficients with high computational accuracy.Furthermore,the PINNs hard constraint and soft constraint methods have their advantages and disadvantages,and the appropriate approach should be selected based on the specific problem and experimental results in practical applications.关键词
物理信息神经网络/潜水/硬约束/软约束/渗流参数反演Key words
Physics-Informed Neural Networks/unconfined flow/hard constraints/soft constraints/inversion of seepage parameters分类
天文与地球科学引用本文复制引用
舒伟,孟胤全,邓芳,蒋建国,吴吉春..基于PINNs算法的一维潜水流方程的渗流参数反演[J].南京大学学报（自然科学版）,2024,60(2):317-327,11.基金项目
国家重点研发计划(2021YFA0715900) （2021YFA0715900）
