396 citations · 673 across the 27 of their papers we have counts for
4 papers · 1 filter
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…
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…
Multivariate Gaussian Process Regression for Multiscale Data Assimilation and Uncertainty Reduction
David A. Barajas-Solano, Alexandre M. Tartakovsky
We present a multivariate Gaussian process regression approach for parameter field reconstruction based on the field's measurements collected at two different scales, the coarse an…
Persistent incomplete mixing in reactive flows
Alexandre M. Tartakovsky, David Barajas-Solano
We present an effective stochastic advection-diffusion-reaction (SADR) model that explains incomplete mixing typically observed in transport with bimolecular reactions. Unlike trad…