Adaptive Physics-Informed Neural Network Modelling Framework for Dual-Porosity Flow

Speaker: Kalyana Babu Nakshatrala, Carl F Gauss Professor and Department Associate Chair of Civil and Environmental Engineering at the University of Houston

Date: 13 July 2026

YouTube link: https://youtu.be/m5_SRM-ekvY

Porous materials are central to many scientific and technological systems, from fractured geological formations and tight shales to engineered foams, biological scaffolds, and plant vascular networks. Across these systems, flow is often governed by two hydraulically coupled pore networks: a highly permeable macro-network that provides preferential pathways, and a finer micro-network that controls storage and inter-network exchange. Accurately modeling such dual-network systems is essential for predictive simulations in subsurface energy, critical-mineral recovery, geological carbon and hydrogen storage, geothermal systems, and engineered porous materials. In this talk, Kalyana Babu Nakshatrala presented an adaptive physics-informed machine learning framework for fluid flow in double-porosity/permeability media. By incrementally updating the model as new data become available, the approach enables continuously refined forecasting of porous media systems. These capabilities support critical-mineral extraction, tight-shale production, carbon dioxide and hydrogen storage-integrity assessment, and geothermal heat-exchange optimisation. The talk concluded by discussing how adaptive scientific machine learning can complement—and, in some settings, transform—traditional computational mechanics workflows for porous media modeling.