466 citations · 1.7k across the 54 of their papers we have counts for
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Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids
Tong Ma, David Alonso Barajas-Solano, Ramakrishna Tipireddy +1
Real-time state estimation and forecasting is critical for efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is pre…
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…