A multifidelity cokriging kernel-learning approach is proposed to construct high-fidelity kernels and means for Gaussian process solutions of nonlinear PDEs, demonstrated on Burgers' equation.
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Finite-dimensional receding horizon control achieves local exponential stabilization of 2D Navier-Stokes equations to reference trajectories, with a POD-based reduced-order model preserving performance at lower cost.
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Multifidelity Gaussian process regression for solving nonlinear partial differential equations
A multifidelity cokriging kernel-learning approach is proposed to construct high-fidelity kernels and means for Gaussian process solutions of nonlinear PDEs, demonstrated on Burgers' equation.
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Finite-Dimensional MOR-Based RHC for Steering 2D Navier-Stokes Equations to Desired Trajectories
Finite-dimensional receding horizon control achieves local exponential stabilization of 2D Navier-Stokes equations to reference trajectories, with a POD-based reduced-order model preserving performance at lower cost.