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We present an application of reduced basis method for Stokes equations in domains with affine parametric dependence.
We discuss the application of the reduced basis method for the simulation and control of unsteady viscous flows governed by the incompressible Navier-Stokes equations.
We present a certified reduced basis method for high-fidelity real-time solution of parametrized partial differential equations on deployed platforms.
Motivated by the recently proposed parallel orbital-updating approach in real space method [1], we propose a parallel orbital-updating based plane-wave basis method for electronic structure calculations, for solving the corresponding eigenvalue problems.
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In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs.
In this work we develop an adaptive and reduced computational algorithm based on dimension-adaptive sparse grid approximation and reduced basis methods for solving high-dimensional uncertainty quantification (UQ) problems.
The topical scope includes, but is not limited to, new out-of-the-box algorithmic solutions for scientific computing, e.g. reduced basis methods for industrial problems and MOR approaches for electrochemical processes.
A learning rate of 0.0005 was used for the Agent and Action basis methods, and 0.0001 for the two REINFORCE methods.
On this basis, a method for analysis of curved beams on frictionless toroids is established.
We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs).
Through numerical experiments, the capability and efficiency of the radial basis functions pseudospectral method for eigenvalue problems are demonstrated, and the numerical accuracy and convergence are examined.
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