arXiv (All)
open access
A differentiable PCA-GPR surrogate framework for full-transient Bayesian inverse uncertainty quantification of black-box nuclear thermal-hydraulic systems
Complex transient energy systems require uncertainty-quantified predictions, but Bayesian inverse uncertainty quantification (IUQ) is often impractical because posterior sampling with high-fidelity system codes requires many expensive full-transient simulations. This study develops a surrogate-assis
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
- Uncertainty Quantification of Next Generation Reservoir Computing with Applications to Memory-Driven Dynamical Systems#1116500
- Exploiting Gradients in Bayesian Inference of Expensive Simulators#1249500
- Accelerating Bayesian inverse design in computational fluid dynamics using neural operators#224506
- Posterior Convergence without Force Convergence: Resolution-Stable Sampling for Rough Bayesian Inverse Problems#998336
- Residual-augmented flow matching operators for probabilistic partial differential equations#973008
Record · ID 1356314
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