Quantifying and Bounding Exotic Matter Requirements in Warp Drive Spacetimes: A Machine Learning Approach with Analytic Lower Bounds

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-27
DOI
https://doi.org/10.5281/zenodo.19898291
Primary Topic
High-Energy Particle Collisions Research
Type
article
Field-Weighted Citation Impact
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article

Quantifying and Bounding Exotic Matter Requirements in Warp Drive Spacetimes: A Machine Learning Approach with Analytic Lower Bounds

Dallas Finley
Zenodo (CERN European Organization for Nuclear Research)
High-Energy Particle Collisions Research
article

Quantifying and Bounding Exotic Matter Requirements in Warp Drive Spacetimes: A Machine Learning Approach with Analytic Lower Bounds

Dallas Finley
article en

Abstract

No abstract available for this paper.
Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 6%
High-Energy Particle Collisions Research
DOIOpen Access PDFOpenAlex
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Quantifying and Bounding Exotic Matter Requirements in Warp Drive Spacetimes: A Machine Learning Approach with Analytic Lower Bounds — Dallas Finley · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS