A Generalized Convection-Diffusion Model for Subgrid Transport in Porous Media. - Yalchin Efendiev; Louis J. Durlofsky (Multiscale Model. Simul., 2003)

Unstructured grid optimization for improved monotonicity of discrete solutions of elliptic equations with highly anisotropic coefficients. - Martin J. Mlacnik; Louis J. Durlofsky (J. Comput. Phys., 2006)

Efficient Incorporation of Global Effects in Upscaled Models of Two-Phase Flow and Transport in Heterogeneous Formations. - Yuguang Chen; Louis J. Durlofsky (Multiscale Model. Simul., 2006)

Use of retrospective optimization for placement of oil wells under uncertainty. - Honggang Wang 002; David Echeverría Ciaurri; Louis J. Durlofsky (WSC, 2010)

Linearized reduced-order models for subsurface flow simulation. - M. A. Cardoso; Louis J. Durlofsky (J. Comput. Phys., 2010)

Application of derivative-free methodologies to generally constrained oil production optimization problems. - David Echeverría Ciaurri; Obiajulu J. Isebor; Louis J. Durlofsky (ICCS, 2010)

Application of derivative-free methodologies to generally constrained oil production optimisation problems. - David Echeverría Ciaurri; Obiajulu J. Isebor; Louis J. Durlofsky (Int. J. Math. Model. Numer. Optimisation, 2011)

Enhanced linearized reduced-order models for subsurface flow simulation. - Jincong He; J. Sætrom; Louis J. Durlofsky (J. Comput. Phys., 2011)

Derivative-Free Optimization for Oil Field Operations. - David Echeverría Ciaurri; Tapan Mukerji; Louis J. Durlofsky (Computational Optimization and Applications in Engineering and Industry, 2011)

Reduced-order flow modeling and geological parameterization for ensemble-based data assimilation. - Jincong He; Pallav Sarma; Louis J. Durlofsky (Comput. Geosci., 2013)

A general method to select representative models for decision making and optimization under uncertainty. - Mehrdad G. Shirangi; Louis J. Durlofsky (Comput. Geosci., 2016)

Trajectory piecewise quadratic reduced-order model for subsurface flow, with application to PDE-constrained optimization. - Sumeet Trehan; Louis J. Durlofsky (J. Comput. Phys., 2016)

Regularized kernel PCA for the efficient parameterization of complex geological models. - Hai X. Vo; Louis J. Durlofsky (J. Comput. Phys., 2016)

Error estimation for surrogate models of dynamical systems using machine learning. - Sumeet Trehan; Kevin Carlberg; Louis J. Durlofsky (CoRR, 2017)

A Deep-Learning-Based Geological Parameterization for History Matching Complex Models. - Yimin Liu; Wenyue Sun; Louis J. Durlofsky (CoRR, 2018)

Implementation and detailed assessment of a GNAT reduced-order model for subsurface flow simulation. - Rui Jiang 005; Louis J. Durlofsky (J. Comput. Phys., 2019)

Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization. - Su Jiang; Louis J. Durlofsky (CoRR, 2020)

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems. - Meng Tang; Yimin Liu; Louis J. Durlofsky (J. Comput. Phys., 2020)

Deep-learning-based coupled flow-geomechanics surrogate model for CO2 sequestration. - Meng Tang; Xin Ju; Louis J. Durlofsky (CoRR, 2021)

Use of low-fidelity models with machine-learning error correction for well placement optimization. - Haoyu Tang; Louis J. Durlofsky (CoRR, 2021)

Data-Space Inversion With a Recurrent Autoencoder for Naturally Fractured Systems. - Su Jiang; Mun-Hong Hui; Louis J. Durlofsky (Frontiers Appl. Math. Stat., 2021)

3D CNN-PCA: A deep-learning-based parameterization for complex geomodels. - Yimin Liu; Louis J. Durlofsky (Comput. Geosci., 2021)

Convolutional-Recurrent Neural Network Proxy for Robust Optimization and Closed-Loop Reservoir Management. - Yong Do Kim; Louis J. Durlofsky (CoRR, 2022)

Multi-Asset Closed-Loop Reservoir Management Using Deep Reinforcement Learning. - Yusuf Nasir; Louis J. Durlofsky (CoRR, 2022)

Surrogate Model for Geological CO2 Storage and Its Use in MCMC-based History Matching. - Yifu Han; François P. Hamon; Su Jiang; Louis J. Durlofsky (CoRR, 2023)

History Matching for Geological Carbon Storage using Data-Space Inversion with Spatio-Temporal Data Parameterization. - Su Jiang; Louis J. Durlofsky (CoRR, 2023)

Use of multifidelity training data and transfer learning for efficient construction of subsurface flow surrogate models. - Su Jiang; Louis J. Durlofsky (J. Comput. Phys., 2023)

Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology. - Yusuf Nasir; Louis J. Durlofsky (J. Comput. Phys., 2023)

Latent diffusion models for parameterization and data assimilation of facies-based geomodels. - Guido Di Federico; Louis J. Durlofsky (CoRR, 2024)

Deep Learning Framework for History Matching CO2 Storage with 4D Seismic and Monitoring Well Data. - Nanzhe Wang; Louis J. Durlofsky (CoRR, 2024)

Accelerated training of deep learning surrogate models for surface displacement and flow, with application to MCMC-based history matching of CO2 storage operations. - Yifu Han; François P. Hamon; Louis J. Durlofsky (CoRR, 2024)

Graph Network Surrogate Model for Optimizing the Placement of Horizontal Injection Wells for CO2 Storage. - Haoyu Tang; Louis J. Durlofsky (CoRR, 2024)

Likelihood-Free Inference and Hierarchical Data Assimilation for Geological Carbon Storage. - Wenchao Teng; Louis J. Durlofsky (CoRR, 2024)

Graph network surrogate model for subsurface flow optimization. - Haoyu Tang; Louis J. Durlofsky (J. Comput. Phys., 2024)

Latent diffusion models for parameterization of facies-based geomodels and their use in data assimilation. - Guido Di Federico; Louis J. Durlofsky (Comput. Geosci., 2025)