AI RESEARCH
An Adaptive Machine Learning Framework for Fluid Flow in Dual-Network Porous Media
arXiv CS.LG
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ArXi:2603.19561v1 Announce Type: cross Porous materials -- natural or engineered -- often exhibit dual pore-network structures that govern processes such as mineral exploration and hydrocarbon recovery from tight shales. Double porosity/permeability (DPP) mathematical models describe incompressible fluid flow through two interacting pore networks with inter-network mass exchange. Despite significant advances in numerical methods, there remains a need for computational frameworks that enable rapid forecasting, data assimilation, and reliable inverse analysis.