160 citations · 162 across the 14 of their papers we have counts for
15 papers
Geometry-aware Latent Autoregressive Generative Model for PDEs in Complex Domains
Zi Wang, Minghui Xu, Tapan Mukerji
Solving multiphysics partial differential equations (PDEs) remains a major challenge in scientific computing, especially for highly complex m-scale tortuous geometries critical…
A convolutional neural network surrogate for hierarchical homogenization: fast elastic moduli prediction of digital rocks
Hanfeng Zhai, Rasool Ahmad, Tapan Mukerji +1
Digital rock physics (DRP) aims to estimate effective rock properties (e.g., elastic moduli) directly from 3D micro-CT images. However, direct numerical simulations (DNS) on high-r…
FMSIM: A Multimodal Flow Matching Framework for Conditional Geomodeling
Jiayuan Huang, Suihong Song, Tapan Mukerji
Subsurface geomodeling plays a critical role in reservoir characterization, uncertainty quantification, and subsurface flow prediction. However, integrating heterogeneous sources o…
DiffSIM: Unconditional and conditional facies simulation based on denoising diffusion generative models
Minghui Xu, Suihong Song, Tapan Mukerji
Constructing subsurface facies models that are geologically plausible and constrained by well facies is essential for analyzing sedimentary evolution, reservoir characterization, a…
Vision Mamba for Permeability Prediction of Porous Media
Ali Kashefi, Tapan Mukerji
Vision Mamba has recently received attention as an alternative to Vision Transformers (ViTs) for image classification. The network size of Vision Mamba scales linearly with input i…
Pix2Geomodel: A Next-Generation Reservoir Geomodeling with Property-to-Property Translation
Abdulrahman Al-Fakih, Ardiansyah Koeshidayatullah, Nabil A. Saraih +4
Accurate geological modeling is critical for reservoir characterization, yet traditional methods struggle with complex subsurface heterogeneity, and they have problems with conditi…
Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes
Jaehong Chung, Michael Manga, Timothy Kneafsey +2
Microearthquakes (MEQs) generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their full spatiotemporal evolution is…
Image registration of 2D optical thin sections in a 3D porous medium: Application to a Berea sandstone digital rock image
Jaehong Chung, Wei Cai, Tapan Mukerji
This study proposes a systematic image registration approach to align 2D optical thin-section images within a 3D digital rock volume. Using template image matching with differentia…
Physics-informed KAN PointNet: Deep learning for simultaneous solutions to inverse problems in incompressible flow on numerous irregular geometries
Ali Kashefi, Tapan Mukerji
Kolmogorov-Arnold Networks (KANs) have gained attention as an alternative to traditional multilayer perceptrons (MLPs) for deep learning applications in computational physics, part…
Seismic inversion using hybrid quantum neural networks
Divakar Vashisth, Rohan Sharma, Tejas Ganesh Iyer +2
Seismic inversion-including post-stack, pre-stack, and full waveform inversion is compute and memory-intensive. Recently, several approaches, including physics-informed machine lea…
Pre-stack and post-stack seismic inversion using quantum computing
Divakar Vashisth, Rodney Lessard, Tapan Mukerji
Quantum computing harnesses the principles of quantum mechanics to solve problems that are intractable for classical computers. Quantum annealing, a specialized approach within qua…
Well log data generation and imputation using sequence-based generative adversarial networks
Abdulrahman Al-Fakih, A. Koeshidayatullah, Tapan Mukerji +2
Well log analysis is crucial for hydrocarbon exploration, providing detailed insights into subsurface geological formations. However, gaps and inaccuracies in well log data, often…
Enhanced anomaly detection in well log data through the application of ensemble GANs
Abdulrahman Al-Fakih, A. Koeshidayatullah, Tapan Mukerji +1
Although generative adversarial networks (GANs) have shown significant success in modeling data distributions for image datasets, their application to structured or tabular data, s…
Accelerating Multiphase Flow Simulations with Denoising Diffusion Model Driven Initializations
Jaehong Chung, Agnese Marcato, Eric J. Guiltinan +4
This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics-based simulations, aiming at reducing the computational costs of f…
Physics-informed PointNet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries
Ali Kashefi, Tapan Mukerji
We present a novel physics-informed deep learning framework for solving steady-state incompressible flow on multiple sets of irregular geometries by incorporating two main elements…