arXiv (All)
open access
Prediction-Oriented Sequential Bayesian Design of Physical Experiments Informed by Computer Models
In many scientific and engineering domains, physical experiments are often costly, time-consuming, and limited to a certain number of design points. The Kennedy and O'Hagan (KOH) framework is widely used for combining simulator runs with limited experimental observations. Under a Bayesian implementa
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
- The Cost of Prediction#95716
- Risk-Aware Goal-Oriented Bayesian Optimal Experimental Design#985423
- FluxDisco: Symbolic Regression for Stoichiometric Dynamical Systems via Monte Carlo Graph Search#972149
- Parallelized adaptive Bayesian updating with structural reliability methods for inference of large engineering models#1161283
- Reproducibility Package for "Temporal Coverage and Forcing Completeness Govern Inference from Dynamic Surface-Tension Curves"#423714
Record · ID 1141504
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