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.
Reduced basis approx imation and a posteriori error estimation for Stokes flows in parametrized geometries: roles of the inf–sup stabi lity constants
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
math.NA 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
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.