most citedAccelerated training of deep learning surrogate models for surface displacement and flow, with application to MCMC-based history matching of CO2 storage operations

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

cs.LG20241 cited

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, Francois P. Hamon, Louis J. Durlofsky

Deep learning surrogate modeling shows great promise for subsurface flow applications, but the training demands can be substantial. Here we introduce a new surrogate modeling frame…

cs.MS2024

Matrix-Free Finite Volume Kernels on a Dataflow Architecture

Ryuichi Sai, Francois P. Hamon, John Mellor-Crummey +1

Fast and accurate numerical simulations are crucial for designing large-scale geological carbon storage projects ensuring safe long-term CO2 containment as a climate change mitigat…

math.NA2024

Pressure-stabilized fixed-stress iterative solutions of compositional poromechanics

Ryan M. Aronson, Nicola Castelletto, François P. Hamon +2

We consider the numerical behavior of the fixed-stress splitting method for coupled poromechanics as undrained regimes are approached. We explain that pressure stability is related…

math.NA2023

Multilevel well modeling in aggregation-based nonlinear multigrid for multiphase flow in porous media

Chak Shing Lee, François P. Hamon, Nicola Castelletto +2

A full approximation scheme (FAS) nonlinear multigrid solver for two-phase flow and transport problems driven by wells with multiple perforations is developed. It is an extension t…

cs.MS2023

Massively Distributed Finite-Volume Flux Computation

Ryuichi Sai, Mathias Jacquelin, François P. Hamon +2

Designing large-scale geological carbon capture and storage projects and ensuring safe long-term CO2 containment - as a climate change mitigation strategy - requires fast and accur…