Irrotational Warp Metrics: Energy Condition Diagnostics and Parameter Space Optimization
Bibliographic record
Abstract
We present a comprehensive numerical study of irrotational (curl-free) shift-vector warp metrics in the ADM formalism, focusing on energy-condition diagnostics and parameter optimization. Using Rodal-style dipole potentials with smoothed wall functions, we compute both fast Eulerian energy densities and invariant diagnostics via Einstein tensor eigenvalues. We demonstrate systematic convergence of 3D volume integrals with tail corrections, validate against literature results (Celmaster & Rubin 2025), and explore superluminal parameter regimes ($v > 1$). Bayesian optimization with Gaussian Process regression efficiently identifies minimal negative-energy configurations, requiring 5$\times$ fewer evaluations than grid search methods. Our results establish quantitative bounds on negative energy requirements for various parameter combinations, providing defensible inputs for theoretical assessments of irrotational warp drive feasibility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".