Volume A40: Constraint Singularities and Critical Points

Constraint singularities and critical points mark locations where constrained systems undergo qualitative changes—bifurcations, phase transitions, or catastrophic failures. This volume presents a unified mathematical framework grounded in differential geometry, topology, and bifurcation theory, classifying singularities from folds and cusps to swallowtails and butterflies. It explores their dynamical consequences across physical, computational, and quantum domains, including phase transitions, computational hardness, and quantum state reconfigurations within the ICQER framework. Stability analysis, symmetry breaking, information geometry, and experimental detection methods are integrated with applications in materials science, network science, and optimization. Control strategies for navigating critical points are surveyed alongside open questions on topological universality and harnessing singularities for AI and quantum computing. This comprehensive treatment bridges rigorous mathematics with interdisciplinary applications for researchers across physics, computer science, engineering, and complex system

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-17
DOI
https://doi.org/10.5281/zenodo.21409125
Primary Topic
Machine Learning in Materials Science
Type
preprint
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Volume A40: Constraint Singularities and Critical Points

Radhakrishnan Jayaraman
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
preprint

Volume A40: Constraint Singularities and Critical Points

Radhakrishnan Jayaraman
preprint en

Abstract

Constraint singularities and critical points mark locations where constrained systems undergo qualitative changes—bifurcations, phase transitions, or catastrophic failures. This volume presents a unified mathematical framework grounded in differential geometry, topology, and bifurcation theory, classifying singularities from folds and cusps to swallowtails and butterflies. It explores their dynamical consequences across physical, computational, and quantum domains, including phase transitions, computational hardness, and quantum state reconfigurations within the ICQER framework. Stability analysis, symmetry breaking, information geometry, and experimental detection methods are integrated with applications in materials science, network science, and optimization. Control strategies for navigating critical points are surveyed alongside open questions on topological universality and harnessing singularities for AI and quantum computing. This comprehensive treatment bridges rigorous mathematics with interdisciplinary applications for researchers across physics, computer science, engineering, and complex system

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
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