A CNN surrogate trained on high-fidelity simulations is embedded in MCMC to infer multiphase flow parameters from full-field FluidFlower experiment observations, yielding better simulation-experiment agreement than manual calibration.
Lithological controls on the permeability of geologic faults: Surrogate modeling and sensitivity analysis.arXiv preprint arXiv:2511.09674, 2025
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Learning and Inferring Multiphase Flow Dynamics in Porous Media using Scientific Machine Learning: Application to the "FluidFlower" CO2 Injection Experiment
A CNN surrogate trained on high-fidelity simulations is embedded in MCMC to infer multiphase flow parameters from full-field FluidFlower experiment observations, yielding better simulation-experiment agreement than manual calibration.