eScholarship
metadata only
Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches
A real-time capable core Ion Cyclotron Range of Frequencies (ICRF) heating model on NSTX and WEST is developed. The model is based on two nonlinear regression algorithms, the random forest ensemble of decision trees and the multilayer perceptron neural network. The algorithms are trained on TORIC IC
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
- Benchmarking classical, ensemble, and deep learning models for soil temperature prediction: A multi-criteria evaluation approach, Türkiye#429528
- Classification of wear mechanisms in hot forming of aluminium alloy by machine and deep learning approaches#288029
- Generating high-resolution climate data in the Andes using artificial intelligence: A lightweight alternative to the WRF model#437597
- Assessment of Machine Learning-Based Critical Heat Flux Models in the CTF Subchannel Code for Square Rod Bundle Prediction#1036383
Record · ID 1145812
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