A Nonlinear Physics-based Reduced Order Model with Convolutional-based Operator Compression
ScientificML
Proposes a nonlinear reduced-order model with convolutional operator compression to overcome the Kolmogorov N-width limitation of linear projection-based ROMs, achieving accurate approximations with fewer modes for parametrized PDEs.
See LICENSE file reduced-order modelconvolutional operatorN-width