REVIEW 3 cited by
Lagrangian basis method for dimensionality reduction of convection dominated nonlinear flows
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Foundations of a new projection-based model reduction approach for convection dominated nonlinear fluid flows are summarized. In this method the evolution of the flow is approximated in the Lagrangian frame of reference. Global basis functions are used to approximate both the state and the position of the Lagrangian computational domain. It is demonstrated that in this framework, certain wave-like solutions exhibit low-rank structure and thus, can be efficiently compressed using relatively few global basis. The proposed approach is successfully demonstrated for the reduction of several simple but representative problems.
Forward citations
Cited by 3 Pith papers
-
Reduced Order Modeling of One-Dimensional Conservative PDEs via the Cumulative Distribution Transform
Mapping solution snapshots to mass-coordinate (CDT) space before POD compresses transport-dominated 1D conservative PDEs into far fewer modes, with proven Kolmogorov-width bounds.
-
Proper Orthogonal Decomposition-based Model-Order Reduction for Smoothed Particle Hydrodynamics Simulation
POD-based model-order reduction can compress SPH simulations of friction stir spot welding to half their degrees of freedom with about one percent error, outperforming uniform particle coarsening.
-
Proper Orthogonal Decomposition-based Model-Order Reduction for Smoothed Particle Hydrodynamics Simulation -- Mass-Spring-Damper System
POD-based model-order reduction reduces degrees of freedom and, with density linearization and coefficient freezing, speeds up Lagrangian SPH mass-spring-damper simulations by 72 to 95 percent while keeping errors acceptable.
Discussion (0). Continue with ORCID to comment.