eScholarship
metadata only
Machine learning enhanced predictions of ICRF heating: Overcoming numerical limitations via data curation
In this work, we present the development of robust surrogate models for Ion Cyclotron Range of Frequencies (ICRF) and High-Harmonic Fast Wave (HHFW) heating predictions in fusion plasmas. Building upon our previous efforts to achieve real-time capable models, we identify the cause of the outliers fo
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
- Automated ICRF heating surrogate modeling via machine learning#1145808
- Data for "Optimizing error field correction while minimizing peak heat fluxes in SPARC"#1167682
- Systematic Effects of Hydrogen and Helium Atmosphere Mismatch on Radius Inference in PSR~J0740+6620-like Synthetic NICER Data#1034587
- Machine learning and AI-driven digital twins in manufacturing of advanced metal alloys and compounds: A review#320276
- High-fidelity modeling of high-enthalpy plasma wind tunnels for hypersonic testing#213274
Record · ID 1145809
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