Peering inside the machine learning black box for fusion experiments

Statistical insights into machine learning analysis can help researchers evaluate model performance and may even provide new physical understanding.

Authors

Publication Details

Journal
Scilight
Published
2026-03-02
DOI
https://doi.org/10.1063/10.0043012
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Peering inside the machine learning black box for fusion experiments

Avery Thompson
Scilight
Machine Learning in Materials Science
article

Peering inside the machine learning black box for fusion experiments

Avery Thompson
article en

Abstract

Statistical insights into machine learning analysis can help researchers evaluate model performance and may even provide new physical understanding.

ScilightVol. 2026(10)
Openalex Percentile: Top 39%
Machine Learning in Materials Science
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Peering inside the machine learning black box for fusion experiments — Avery Thompson · Scilight (2026) | TGRS Research Map | TGRS