Quadratic-manifold Neural Galerkin reduced models give locally unique, residual-minimizing trajectories and, for linear full models, online cost independent of the full dimension.
Peherstorfer, Breaking the Kolmogorov barrier with nonlinear model reduction, Notices of the American Mathematical Society 69 (5) (2022) 725–733
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
background 1representative citing papers
citing papers explorer
-
Nonlinear model reduction with Neural Galerkin schemes on quadratic manifolds
Quadratic-manifold Neural Galerkin reduced models give locally unique, residual-minimizing trajectories and, for linear full models, online cost independent of the full dimension.