Mass as a Domain Dimensional Projection Extending Hidden Dimension Models with Generative Architecture

This paper develops a unified framework in which mass emerges as a domain‑dimensional projection within a generative architectural structure. Traditional hidden‑dimension models treat mass as a geometric artifact of compactified or inaccessible dimensions. This work extends that idea by showing that dimensions and generative domains can be treated as equivalent structural axes, allowing mass to be expressed as a projection arising from torsion between interacting domains.The paper formalizes this projection mechanism, demonstrating how mass values correspond to stable configurations within the 1–5–5–5 generative architecture. By reframing hidden dimensions as active generative components rather than passive geometric leftovers, the model provides a deeper explanation for mass, inertia, and domain‑coupled interactions. This approach generalizes earlier torsion‑based mass proposals and embeds them within a broader, self‑consistent architectural system.The result is a conceptual bridge between hidden‑dimension physics and generative domain theory, offering a scalable, extensible structure for understanding mass as an emergent property of domain‑dimensional alignment.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-13
DOI
https://doi.org/10.5281/zenodo.18189744
Primary Topic
Architecture and Computational Design
Type
article
Field-Weighted Citation Impact
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article

Mass as a Domain Dimensional Projection Extending Hidden Dimension Models with Generative Architecture

Brian Rieckmann
Zenodo (CERN European Organization for Nuclear Research)
Architecture and Computational Design
article

Mass as a Domain Dimensional Projection Extending Hidden Dimension Models with Generative Architecture

Brian Rieckmann
article en

Abstract

This paper develops a unified framework in which mass emerges as a domain‑dimensional projection within a generative architectural structure. Traditional hidden‑dimension models treat mass as a geometric artifact of compactified or inaccessible dimensions. This work extends that idea by showing that dimensions and generative domains can be treated as equivalent structural axes, allowing mass to be expressed as a projection arising from torsion between interacting domains.The paper formalizes this projection mechanism, demonstrating how mass values correspond to stable configurations within the 1–5–5–5 generative architecture. By reframing hidden dimensions as active generative components rather than passive geometric leftovers, the model provides a deeper explanation for mass, inertia, and domain‑coupled interactions. This approach generalizes earlier torsion‑based mass proposals and embeds them within a broader, self‑consistent architectural system.The result is a conceptual bridge between hidden‑dimension physics and generative domain theory, offering a scalable, extensible structure for understanding mass as an emergent property of domain‑dimensional alignment.

Zenodo (CERN European Organization for Nuclear Research)
Sustainable cities and communities
Openalex Percentile: Top 91%
Architecture and Computational Design
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Mass as a Domain Dimensional Projection Extending Hidden Dimension Models with Generative Architecture — Brian Rieckmann · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS