IDEALS @ University of Illinois Urbana-Champaign
metadata only
Multi-fidelity machine learning methods for sputtering yield calculations relevant to magnetic fusion energy systems
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01
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
- Reliable Uncertainty Estimation for Machine-Learned Multigroup Cross Sections Without Retraining#1233270
- PHASE: Multi-Regime Modeling of Incompressible Magnetohydrodynamics#1141924
- AI-assisted Fusion Modelling (Magnetic Fusion Energy)#495400
- Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science#1341166
- Is Your AI Fast Enough to Run a Fusion Reactor?#1155780
Record · ID 1390917
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