A new reduced-order modeling framework uses tensor-train decomposition of finite element snapshots to cut offline costs and reduce subspace dimensions for parameterized PDEs on Cartesian grids.
Model order reduction with nove l discrete empirical interpolation methods in space–time
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A tensor-train reduced basis solver for parameterized partial differential equations on Cartesian grids
A new reduced-order modeling framework uses tensor-train decomposition of finite element snapshots to cut offline costs and reduce subspace dimensions for parameterized PDEs on Cartesian grids.