人工智能技术(AI)在水沙数学模型中的应用OA

Mathematical models of sediment transport,grounded in physical governing equations,are essential tools for understanding sediment transport processes and supporting basin management and hydraulic engineering.How-ever,conventional numerical methods are often constrained by computational efficiency and thus struggle to meet the demands of emerging scenarios such as digital twins for real-time,high-efficiency prediction and intelligent decision-making.To address this issue,this study reviews three technical routes-data assimilation,data-driven neural net-works,and physics-informed neural networks-within an analytical framework that spans input boundaries and param-eters,governing equations,and model outputs.The results show that:(1)data assimilation can effectively suppress the propagation of errors in initial and boundary conditions,enhancing forecast stability and accuracy;(2)data-driven neural networks can markedly reduce computation time and are suitable for short-term,rapid prediction tasks;and(3)physics-informed neural networks demonstrate better physical consistency under data-scarce condi-tions but face challenges in convergence and accuracy when addressing high-dimensional,multi-scale problems.Based on these findings,this paper proposes an integrated approach oriented to engineering applications,which cen-ters on hybrid physics-data modeling combined with online correction through data assimilation,aiming to balance efficiency and accuracy and to provide a reference for real-time updating and intelligent decision-making in digital-twin sediment-water systems.