Holding a plasma in shape.

↙ Return to the open questionsBright record 013 · ScienceInspect the source ↓

The human problem

Hot plasma must be controlled within a complex magnetic system.

What changed

The constraint: Controllers require specialized engineering for target configurations.
AI’s role: A trained policy commanded the tokamak’s magnetic coils.
The observed result: The controller demonstrated multiple plasma configurations on TCV.

The human possibility

A tool for researchers exploring fusion reactor designs.

Possible implications are separate from measured outcomes.

Keep following the question.

What holds this up

Magnetic control of tokamak plasmas through deep reinforcement learning ↗

Event date: Not separately established
Source publication: 2022-02-16
Capture date: Not retained in the original record
Claim confidence: Unassessed. Separate from maturity; no numeric certainty inferred.
Review provenance: Legacy source check. No named human editorial approval is recorded.
Evidence state: Demonstrated · What this means
Source checked: 2026-09-05Compared with the published record. No independent replication by Bright.
Replication: Not assessed by Bright
Conflicting findings: Not systematically reviewed

The limits are part of the record.

Plasma control is not net energy production or commercial fusion.

What this classification does—and does not—establish

Bright’s classification applies to the result described here. It does not certify a product, recommend a treatment, or predict a future outcome. Source publication and real-world deployment are different milestones. This opening record may not include every later study.

A record that can change

2026-09-05
Added to the source-checked opening collection.

This is Bright’s catalog history, not a complete history of the research. Future corrections and evidence revisions will be recorded here.

What comes next

Test broader conditions and more demanding devices.

How corrections change the record ↗