Talk title: Adaptive Physics-Informed Neural Network Modeling Framework for Dual-Porosity Flow
Speaker: Professor Kalyana Babu Nakshatrala, University of Houston
Date: 13 July 2026 (Monday)
Time: 04:00 PM (IST)
Venue: #102, Department of Computational and Data Sciences, IISc/online Teams Meet*
Registration: https://forms.cloud.microsoft/r/GP4ch3mhEd (registration is free but mandatory)
*the Teams Meet link will be sent to the registered email ID
Abstract:
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.
This talk presents an adaptive physics-informed machine learning framework for fluid flow in double-porosity/permeability media. The approach embeds the mixed-form governing equations for coupled pressure and velocity fields directly into a physics-informed neural network, enabling both forward prediction and inverse parameter identification. To resolve the multiscale features and sharp solution gradients typical of dual-network porous media, the framework combines a shared-trunk neural architecture with adaptive loss weighting and error-guided collocation-point refinement. Together, these elements balance competing physical constraints, focus learning in high-residual regions, and preserve coupling between macro- and micro-pore networks.
Representative numerical studies show that this mesh-free formulation captures discontinuities across layered media, remains stable under large permeability contrasts, and avoids spurious oscillations often encountered in classical mixed finite element schemes. Beyond forward simulation, the framework provides a natural platform for assimilating sparse experimental, field, or sensor data to infer difficult-to-measure quantities such as permeability fields and inter-porosity transfer coefficients.
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 concludes by discussing how adaptive scientific machine learning can complement—and, in some settings, transform—traditional computational mechanics workflows for porous media modeling.
Biography:
Kalyana Babu Nakshatrala is the Carl F Gauss Professor and Department Associate Chair of Civil and Environmental Engineering at the University of Houston, with a courtesy appointment in Mechanical Engineering. His research and teaching have been recognised with several honours, including the Andrea Prosperetti Research Computing Faculty Award, the Kittinger Teaching Excellence Award, and the University of Houston Teaching Excellence Award. He previously served as a Faculty Associate at Caltech and completed postdoctoral work at UIUC in collaboration with Los Alamos and Pacific Northwest National Laboratories. He holds a PhD in Civil Engineering, MS degrees in Civil Engineering and Applied Mathematics, and a certificate in Computational Science and Engineering from UIUC, as well as a Bachelor’s degree from IIT Madras. He is an Associate Editor of ASCE’s Journal of Engineering Mechanics and serves on the EMSL User Executive Committee.
Host faculty: Professor Soumyendu Raha, Department of Computational and Data Sciences, IISc
I joined the BTech programme in Mathematics and Computing at the Indian Institute of Science in 2022. My faculty advisor, Professor Viraj Kumar, has guided my academic journey, and I had the opportunity to work under his mentorship during the summer of 2024.
I joined BTech at IISc in 2024.
I joined the BTech programme in 2024. My faculty advisor is Professor Viraj Kumar. I am interested in computer science and would like to do research in computer science in the future.
Anmol Gill pursues the MTech (Artificial Intelligence) programme at IISc, in the Robert Bosch Centre for Cyber-Physical Systems (
I recently completed my BTech in August 2024 from the Vellore Institute of Technology, Chennai, with a major in Electronics and Communication Engineering. From May 2024, I am a research intern, supported by the Kotak-IISc AI/ML Centre, in the Human-Interactive Robotics (HiRo) Lab at the Robert Bosch Centre for Cyber-Physical Systems, IISc. I am working under the guidance of Professor Ravi Prakash. My essential area of interest lies in robot learning, which lies under the general area of AI and robotics.
