Parareal with a coarse solver built from low-rank SVD approximations of the fine solver accelerates convergence for parabolic PDEs, with error bounds depending on the truncated singular values.
pyMOR – Generic Algorithms and Interfaces for Model Order Reduction
8 Pith papers cite this work, alongside 86 external citations. Polarity classification is still indexing.
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An adaptive reduced-basis trust-region Gauss-Newton method is extended to parabolic parameter identification, with POD-based enrichment, achieving 5 to 18x speedups in four reaction-diffusion tests.
A linear-program dead time splitting scheme plus an adaptive randomized Eigensystem Realization Algorithm yields smaller and more accurate reduced order models from large room impulse response datasets.
pyMOR now unifies model-based and data-driven model order reduction in one open Python framework, demonstrated on Navier-Stokes and mass-spring-damper benchmarks.
Two new DOD-based reduced-order models (DOD-DL-ROM and DOD+DFNN) are introduced for hybrid-type parabolic PDEs, with rigorous error bounds linking performance to optimal map regularity and conditions for outperforming POD methods.
Adaptive reduced-basis trust-region framework for efficient IRGNM-based defect identification in hyperbolic elastic systems, extending prior elliptic/parabolic work.
A tutorial-style paper that details and demonstrates a fully distributed implementation of Operator Inference for building reduced-order models from datasets too large for a single computer.
A localized model order reduction methodology with certified error estimation and randomized training that provably converges nearly as fast as the singular value decay of a transfer operator.
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A New Adaptive Deep Learning based Reduced Order Model for Hybrid-Type Parabolic PDEs: Rigorous Error Analysis and Applications
Two new DOD-based reduced-order models (DOD-DL-ROM and DOD+DFNN) are introduced for hybrid-type parabolic PDEs, with rigorous error bounds linking performance to optimal map regularity and conditions for outperforming POD methods.