Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution - Perez, Sarah; Poncet, Philippe (Computational geosciences, 2024)
Keywords: Bayesian analysis; Bayesian theory; Calcite; Calcite dissolution; Carbon capture and storage; Carbon sequestration; Data assimilation; Data collection; Differential equations; Dimensionless numbers; Dissolution; Dissolving; Earth and Environmental Science; Earth Sciences; Engineering Sciences; Fluid mechanics; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Imaging techniques; Inverse problems; Mathematical Modeling and Industrial Mathematics; Mathematical models; Mathematics; Mechanics; Modelling; Multitasking; Neural networks; Numerical Analysis; Observational weighting; Operators (mathematics); Original Paper; Parameter estimation; Parameter uncertainty; Parameters; Physics; Porosity; Probability theory; Reactive fluid environment; Robustness; Soil Science & Conservation; Statistical inference; Uncertainty; Workflow; X ray imagery; X ray microtomography; X rays; Bayesian Physics-Informed Neural Networks; Hamiltonian Monte Carlo; Pore-scale porous media; Multi-objective training; Uncertainty quantification; Imaging inverse problem; Artificial intelligence; Uncertainty Quantification; Artificial Intelligence