arXiv (All)
open access
Learning Informative Prior with Infinite-Dimensional Continuous Normalizing Flow for Bayesian Inverse Problem
This paper addresses infinite-dimensional Bayesian inference for inverse problem of partial differential equations with model parameters in infinite-dimensional Hilbert space. To effectively incorporate prior information, we propose a novel continuous normalizing flows based infinite-dimensional mod
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
- Perturb-and-Solve: Efficient Learned-Operator Conditioning for Latent Diffusion Inverse Problems#1117883
- Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference#926968
- RoomLight: A 2.5D Illumination Prior for Indoor Environments#1064844
- Physically-Relevant Information Learning in High-Dimensional Time-Derivatives Spaces#343499
- Active subspace learning for coarse-grained molecular dynamics#485333
Record · ID 926271
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