Interactive, multiobjective Bayesian optimization of tokamak scenarios
Bibliographic record
Abstract
Bayesian optimization is applied to tokamak scenario optimizations. The key advantages are 1) a reduced number of objective function evaluations, 2) no need for derivatives, and 3) the possibility to include a prior knowledge. This is of a great value for optimizing tokamak scenarios, where several (competing) objectives with often unknown magnitudes exist and the number of parameters is large (>10). The first two properties imply that Bayesian optimization is well suited for heavy, complex objective functions. Reusing previous iterations as priors for next optimization steps effectively enables interactive, multiobjective optimizations, regardless of whether a human decision maker is included or not. We show that these features make Bayesian optimization an outstanding tool for optimizing tokamak scenarios. Objective functions and constraints, targeting, e.g., fusion gain, flux consumption, coils currents limits or q-profile, can be assembled interactively. The optimized parameter vector may include actuators like plasma current or heating waveforms. We demonstrate the capabilities on optimizing ITER and DEMO-like scenarios, simulated by the METIS code
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".