466 citations · 1.7k across the 54 of their papers we have counts for
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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…
Discrete-element model for the interaction between ocean waves and sea ice
Zhijie Xu, Alexandre M. Tartakovsky, Wenxiao Pan
We present a discrete element method (DEM) model to simulate the mechanical behavior of sea ice in response to ocean waves. The interaction of ocean waves and sea ice can potential…
A dissipative particle dynamics model of biofilm growth
Zhijie Xu, Paul Meakin, Alexandre Tartakovsky +1
A dissipative particle dynamics (DPD) model for the quantitative simulation of biofilm growth controlled by substrate (nutrient) consumption, advective and diffusive substrate tran…
A diffuse-interface model for smoothed particle hydrodynamics
Zhijie Xu, Paul Meakin, Alexandre Tartakovsky
Diffuse-interface theory provides a foundation for the modeling and simulation of microstructure evolution in a very wide range of materials, and for the tracking/capturing of dyna…