An iSVD-based adaptive ROM framework updates reduced bases with occasional full-order snapshots, showing improved accuracy and efficiency over direct adaptation baselines on Burgers, Sod, and rotating detonation engine problems.
Real-time reduced-order modeling of stochastic partial differential equations via time-dependent subspaces,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A dynamic subspace method parameterizes low-dimensional bases as geodesic paths on the Grassmannian to track evolving physics in nonlinear systems, achieving higher accuracy than static approximations at the same rank.
citing papers explorer
-
History-aware adaptive reduced-order models via incremental singular value decomposition
An iSVD-based adaptive ROM framework updates reduced bases with occasional full-order snapshots, showing improved accuracy and efficiency over direct adaptation baselines on Burgers, Sod, and rotating detonation engine problems.
-
A Dynamic Subspace Approach for Low-rank Approximation of Large-scale Nonlinear Systems
A dynamic subspace method parameterizes low-dimensional bases as geodesic paths on the Grassmannian to track evolving physics in nonlinear systems, achieving higher accuracy than static approximations at the same rank.