An Adaptive Machine Learning Framework for Fluid Flow in Dual-Network Porous Media
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. To address this, we present a physics-informed neural network (PINN) framework for forward and inverse modeling of DPP systems. The proposed approach encodes the governing...
| Authors | V. S. Maduri, K. B. Nakshatrala |
|---|---|
| Primary category | math.NA |
| Published | 20 March 2026 |
| Last updated at source | 23 September 2026 |
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