Title: Predictive Geometry with Wave Function Collapse

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the application of wave function collapse theory, traditionally confined to quantum mechanics, to geometric modeling. We propose a novel framework for predicting geometric properties – such as surface area, volume, and regularity – based on initial conditions and iterative collapse of wave functions. The core mechanism involves a system that systematically collapses wave functions, generating geometric representations that are then used to quantify and predict geometric characteristics. This approach seeks to bridge the gap between quantum mechanics and geometric analysis, offering a potential pathway for automated geometric design and analysis. The fundamental challenge lies in efficiently implementing and optimizing the collapse process, while maintaining the stability and accuracy of the resulting geometric models. This work investigates a specific algorithm for iterative collapse, focusing on its potential for generating meaningful and consistent geometric representations.

DOI: 10.5281/zenodo.22263220

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