arXiv (All)
open access
A Novel Multi-fidelity Surrogate for Efficient Turbine Design Optimization
To solve the turbine design optimization problems efficiently, surrogate-based optimization (SBO) algorithms are frequently used. To further reduce the cost of turbine design, the multi-fidelity surrogate (MFS) based optimization is proposed by the researchers, who resort to augmenting the small num
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 Turbomachinery Design Optimization#998666
- Synergistic ANN-GA-CFD framework for high-performance Savonius wind turbine optimization with experimental validation.#218869
- Optimization and Experimental Validation of Savonius Turbines with Integrated Deflector for Enhanced Low-Speed Hydropower Efficiency#333943
- MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimization#1078012
- Adaptive multi-fidelity optimization with fast learning rates#31260
Record · ID 998667
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