222 citations · 372 across the 17 of their papers we have counts for
6 papers · 1 filter
Generating Multiphase Fluid Configurations in Fractures using Diffusion Models
Jaehong Chung, Agnese Marcato, Eric J. Guiltinan +3
Pore-scale simulations accurately describe transport properties of fluids in the subsurface. These simulations enhance our understanding of applications such as assessing hydrogen…
Prediction of Effective Elastic Moduli of Rocks using Graph Neural Networks
Jaehong Chung, Rasool Ahmad, WaiChing Sun +2
This study presents a Graph Neural Networks (GNNs)-based approach for predicting the effective elastic moduli of rocks from their digital CT-scan images. We use the Mapper algorith…
Homogenizing elastic properties of large digital rock images by combining CNN with hierarchical homogenization method
Rasool Ahmad, Mingliang Liu, Michael Ortiz +2
Determining effective elastic properties of rocks from their pore-scale digital images is a key goal of digital rock physics (DRP). Direct numerical simulation (DNS) of elastic beh…
ChatGPT for Programming Numerical Methods
Ali Kashefi, Tapan Mukerji
ChatGPT is a large language model recently released by the OpenAI company. In this technical report, we explore for the first time the capability of ChatGPT for programming numeric…
Physics-informed PointNet: On how many irregular geometries can it solve an inverse problem simultaneously? Application to linear elasticity
Ali Kashefi, Leonidas J. Guibas, Tapan Mukerji
Regular physics-informed neural networks (PINNs) predict the solution of partial differential equations using sparse labeled data but only over a single domain. On the other hand,…
Bayesian Geophysical Basin Modeling with Seismic Kinematics Metrics to Quantify Uncertainty for Pore Pressure Prediction
Josue Fonseca, Anshuman Pradhan, Tapan Mukerji
Bayesian geophysical basin modeling (BGBM) methodology is an interdisciplinary workflow that incorporates data, geological expertise, and physical processes through Bayesian infere…