arXiv (All)
open access
Neural Approximate Inverse Preconditioners
In this paper, we propose a learning-based framework for constructing approximate inverse preconditioners for partial differential equations (PDEs) by approximating the Green's function of the underlying differential operator with neural networks (NNs). The training framework combines four component
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
- Physics-informed Learning for Orbital Uncertainty Propagation with Error Bounds#822301
- PDE-constrained inverse problems at the $\sqrt{n}$ rate via debiased physics-informed neural networks#1000259
- Adjoint Method versus Physics-Informed Neural Networks in PDE-Constrained Inverse Problems#665808
- Adjoint Method versus Physics-Informed Neural Networks in PDE-Constrained Inverse Problems#664308
- Gradient-based linear and nonlinear dimension reduction#796302
Record · ID 1247300
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