G. Tartakovsky

Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems

Alexandre M. Tartakovsky, Carlos Ortiz Marrero, Paris Perdikaris et al. · Water Resources Research · 2020 · 433 citations · Full text

Learning Parameters, Constitutive Relationships, Reservoir Simulation +15

Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport

Qizhi He, David A. Barajas‐Solano, G. Tartakovsky et al. · Advances in Water Resources · 2020 · 345 citations · Full text

Numerical Analysis, Engineering, Physics +13

Three‐dimensional saturated‐unsaturated flow with axial symmetry to a partially penetrating well in a compressible unconfined aquifer

G. Tartakovsky, Shlomo P. Neuman · Water Resources Research · 2007 · 137 citations

Geotechnical Engineering, Hydrogeology, Symmetric Flow +15

Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence

Xiu Yang, David A. Barajas‐Solano, G. Tartakovsky et al. · Journal of Computational Physics · 2019 · 86 citations · Full text

Uncertainty Quantification, Statistics, Physics-informed Cokriging +1

Pore-scale and continuum simulations of solute transport micromodel benchmark experiments

M. Oostrom, Yashar Mehmani, Pedro Romero–Gomez et al. · Computational Geosciences · 2014 · 77 citations · Full text

Pore Structure, Engineering, Diffusion Resistance +13