A library of training images for fluvial and deepwater reservoirs and associated code. - Michael J. Pyrcz; Jeff B. Boisvert; Clayton V. Deutsch (Comput. Geosci., 2008)

ALLUVSIM: A program for event-based stochastic modeling of fluvial depositional systems. - Michael J. Pyrcz; Jeff B. Boisvert; Clayton V. Deutsch (Comput. Geosci., 2009)

Conditioning 3D object-based models to dense well data. - Yimin C. Wang; Michael J. Pyrcz; Octavian Catuneanu; Jeff B. Boisvert (Comput. Geosci., 2018)

A comparison of clustering algorithms applied to fluid characterization using NMR T1-T2 maps of shale. - Han Jiang; Hugh Daigle; Xiao Tian; Michael J. Pyrcz; Chris Griffith; Boyang Zhang (Comput. Geosci., 2019)

Modeling nanoconfinement effects using active learning. - Javier E. Santos; Mohammed Mehana; Hao Wu; Masa Prodanovic; Michael J. Pyrcz; Qinjun Kang; Nicholas Lubbers; Hari S. Viswanathan (CoRR, 2020)

Multi-Scale Neural Networks for to Fluid Flow in 3D Porous Media. - Javier E. Santos; Ying Yin; Honggeun Jo; Wen Pan; Qinjun Kang; Hari S. Viswanathan; Masa Prodanovic; Michael J. Pyrcz; Nicholas Lubbers (CoRR, 2021)

Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learning. - Aidan Petratos; Allen Ting; Shankar Padmanabhan; Kristina Zhou; Dylan Hageman; Jesse R. Pisel; Michael J. Pyrcz (CoRR, 2021)

Automatic Feature Highlighting in Noisy RES Data With CycleGAN. - Nicholas Khami; Omar Imtiaz; Akif Abidi; Akash Aedavelli; Alan Goff; Jesse R. Pisel; Michael J. Pyrcz (CoRR, 2021)

StackGAN: Facial Image Generation Optimizations. - Badr Belhiti; Justin Milushev; Avinash Gupta; John Breedis; Johnson Dinh; Jesse R. Pisel; Michael J. Pyrcz (CoRR, 2021)

Optimizing Oil and Gas Acquisitions Using Recommender Systems. - Harsh Kumar; Geneva Allison; Jehil Mehta; Jesse R. Pisel; Michael J. Pyrcz (CoRR, 2021)

Machine learning-based porosity estimation from spectral decomposed seismic data. - Honggeun Jo; Yongchae Cho; Michael J. Pyrcz; Hewei Tang; Pengcheng Fu (CoRR, 2021)

A recommender system for automatic picking of subsurface formation tops. - Jesse R. Pisel; Joshua A. Dierker; Sanya Srivastava; Samira B. Ravilisetty; Michael J. Pyrcz (CoRR, 2022)

Physics-Informed Graph Neural Network for Spatial-temporal Production Forecasting. - Wendi Liu; Michael J. Pyrcz (CoRR, 2022)

MPLBM-UT: Multiphase LBM library for permeable media analysis. - Javier E. Santos; Alex Gigliotti; Abhishek Bihani; Christopher Landry; Marc Andre Hesse; Michael J. Pyrcz; Masa Prodanovic (SoftwareX, 2022)

Rigid Transformations for Stabilized Lower Dimensional Space to Support Subsurface Uncertainty Quantification and Interpretation. - Ademide O. Mabadeje; Michael J. Pyrcz (CoRR, 2023)

Self-Supervised Learning for Seismic Data: Enhancing Model Interpretability With Seismic Attributes. - Jose Julian Salazar; Eduardo Maldonado-Cruz; Jesus Ochoa; Michael J. Pyrcz (IEEE Trans. Geosci. Remote. Sens., 2023)

A new methodology using borehole data to measure angular distances between geological interfaces. - Michal P. Michalak; Pawel Marzec; Filip Turobos; Paulina Leonowicz; Leslaw Teper; Pawel Gladki; Michael J. Pyrcz; Mariusz Szubert (Earth Sci. Informatics, 2023)

Mitigation of spatial nonstationarity with vision transformers. - Lei Liu; Javier E. Santos; Masa Prodanovic; Michael J. Pyrcz (Comput. Geosci., 2023)

Evaluating the Stability of Deep Learning Latent Feature Spaces. - Ademide O. Mabadeje; Michael J. Pyrcz (CoRR, 2024)

Spatial bagging to integrate spatial correlation into ensemble machine learning. - Fehmi Özbayrak; John T. Foster; Michael J. Pyrcz (Comput. Geosci., 2024)

Anisotropic resistivity estimation and uncertainty quantification from borehole triaxial electromagnetic induction measurements: Gradient-based inversion and physics-informed neural network. - Misael M. Morales; Ali Eghbali; Oriyomi Raheem; Michael J. Pyrcz; Carlos Torres-Verdín (Comput. Geosci., 2025)

Spatial bagging for predictive machine learning uncertainty quantification. - Fehmi Özbayrak; John T. Foster; Michael J. Pyrcz (Comput. Geosci., 2025)

PointSSIM: A novel low dimensional resolution invariant image-to-image comparison metric. - Oscar Ovanger; Ragnar Hauge; Jacob Skauvold; Michael J. Pyrcz; Jo Eidsvik (CoRR, 2025)