arXiv (All)
open access
A Novel Multi-fidelity Surrogate for Turbomachinery Design Optimization
Turbomachinery design optimization involves expensive black-box problems. Sample-efficient multi-fidelity optimization (MFO) offers an efficient solution. By utilizing multi-fidelity surrogates (MFS), the MFO algorithm can use fewer high-fidelity samples aided by low-fidelity samples to establish an
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
- A Novel Multi-fidelity Surrogate for Efficient Turbine Design Optimization#998667
- Adaptive multi-fidelity optimization with fast learning rates#31260
- Rethinking Learnability in Offline Data-driven Optimization#822048
- KLineage: Recovering the Missing When of Kernel Optimization by Deoptimizing Experts#1141552
- Synergistic ANN-GA-CFD framework for high-performance Savonius wind turbine optimization with experimental validation.#218869
Record · ID 998666
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