Model reduction of traveling-wave problems via Radon cumulative distribution transform

Due to their sizeable Kolmogorov n-width, travel-wave problems have brought critical challenges conventional model reduction techniques. This study aims provide new insights into this problem by exploiting the Radon cumulative distribution transform (R-CDT) that emerged in sector of computer vision science. By virtue unique property nonlinear invertible R-CDT renders both traveling and scaling components amplitude modulations, a substantial model-reduction is achieved space, while sustaining high accuracy. The method parameter-free data-driven, which lends itself regardless dimensions or boundary conditions.