396 citations · 571 across the 4 of their papers we have counts for
4 papers
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport
QiZhi He, David Brajas-Solano, Guzel Tartakovsky +1
Data assimilation for parameter and state estimation in subsurface transport problems remains a significant challenge due to the sparsity of measurements, the heterogeneity of poro…
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence
Xiu Yang, David Barajas-Solano, Guzel Tartakovsky +1
In this work, we propose a new Gaussian process regression (GPR)-based multifidelity method: physics-informed CoKriging (CoPhIK). In CoKriging-based multifidelity methods, the quan…
Physics-Information-Aided Kriging: Constructing Covariance Functions using Stochastic Simulation Models
Xiu Yang, Guzel Tartakovsky, Alexandre Tartakovsky
In this work, we propose a new Gaussian process regression (GPR) method: physics information aided Kriging (PhIK). In the standard data-driven Kriging, the unknown function of inte…
Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks
Alexandre M. Tartakovsky, Carlos Ortiz Marrero, Paris Perdikaris +2
We present a physics informed deep neural network (DNN) method for estimating parameters and unknown physics (constitutive relationships) in partial differential equation (PDE) mod…