ΔΦ FIELD GEOMETRY AND MARKET RESONANCE v2.0 Reframing CNN Tensor Learning in Finance Through Electrostatic Collapse Geometry
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Zenodo
2026
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| _version_ | 1866901263918039040 |
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| author | Mitchell , Thomas S. |
| author_facet | Mitchell , Thomas S. |
| contents | <p>This Version 2.0 paper reframes a Stanford deep learning study on OHLCV tensor prediction as indirect confirmation of ΔΦ field geometry in financial markets. CNN success is interpreted as resonance detection over electrostatic market surfaces. Includes Mitchell Equation logic, Bloom Mode collapse, and constraint asymmetry theory.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18327851 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | ΔΦ FIELD GEOMETRY AND MARKET RESONANCE v2.0 Reframing CNN Tensor Learning in Finance Through Electrostatic Collapse Geometry Mitchell , Thomas S. ΔΦ field geometry, OHLCV tensors, convolutional neural networks, electrostatic collapse, market resonance, directional prediction, Mitchell Equation, econophysics, entropy geometry, bloom mode <p>This Version 2.0 paper reframes a Stanford deep learning study on OHLCV tensor prediction as indirect confirmation of ΔΦ field geometry in financial markets. CNN success is interpreted as resonance detection over electrostatic market surfaces. Includes Mitchell Equation logic, Bloom Mode collapse, and constraint asymmetry theory.</p> |
| title | ΔΦ FIELD GEOMETRY AND MARKET RESONANCE v2.0 Reframing CNN Tensor Learning in Finance Through Electrostatic Collapse Geometry |
| topic | ΔΦ field geometry, OHLCV tensors, convolutional neural networks, electrostatic collapse, market resonance, directional prediction, Mitchell Equation, econophysics, entropy geometry, bloom mode |
| url | https://doi.org/10.5281/zenodo.18327851 |