ΔΦ FIELD GEOMETRY AND MARKET RESONANCE v2.0 Reframing CNN Tensor Learning in Finance Through Electrostatic Collapse Geometry

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Main Author: Mitchell , Thomas S.
Format: Recurso digital
Published: Zenodo 2026
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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
language
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