arXiv (All)
open access
Additive reduced basis preconditioners for large-scale parametrized PDEs
We introduce a class of additive reduced basis preconditioners designed to accelerate the iterative solution of large-scale linear systems arising from discretized parametrized PDEs. The main idea is to regularize the inherently singular reduced-order approximation by adding simple correction terms:
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Tackling Parallelization Challenges of Randomized Preconditioners With Dependency Tracking#997434
- Spectral analysis of a class of IRK stage preconditioners for linear wave equations#1128882
- Scalable Approximate Selected Inversion Based on Single-Level Incomplete $LDL^T$ Factorization and Spectral Corrections for Large Sparse Systems#998898
- Balance-Guided Sparse Identification of Multiscale Nonlinear PDEs with Small-coefficient Terms#120530
- Hierarchical matrix preconditioning for convection dominated problems#330289
Record · ID 998653
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive