Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.03557v1</id>\n    <title>A Pathway Selection Process for Dynamically Self-Organizing Systems</title>\n    <updated>2026-03-03T22:15:50Z</updated>\n    <link href='https://arxiv.org/abs/2603.03557v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.03557v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>Self-organization creates new order and shifts sub-boundaries while reorganizing energy and entropy within a control volume. This article examines pathway selection and tests whether maximizing the entropy generation rate can forecast process pathways. All entropy-generating processes distribute internal energy through temperature changes or structural responses, thereby creating new patterns or causing volume changes. Rapid self-organization, such as a supercooled liquid metal transforming into a solid, is a quasi-adiabatic process that tends to approach equilibrium or a steady state with respect to parameters like temperature. This is one of the main examples studied. Entropy generation is linked to internal energy redistribution, either as work performed (called stored work) or as thermal energy stored within a system. A system's resilience during and after self-organization is reflected in the emergence of measurable engineering properties. In the examples studied, the entropy generation rate is maximized throughout the process, regardless of the work needed to create new boundaries. Self-organization is a dissipative process, linked to pattern formation. The article discusses various patterns and shapes in physical systems, including grain size and morphology during thermo-mechanical deformation of crystalline solids, solid-liquid transformations, atmospheric effects, fluid-flow eddies, and patterned flight in birds that conserve energy within the framework of entropy-rate maximization. Morphological boundary limits are examined in terms of the ratio of the energy dissipation rate to the entropy generation rate for several examples. Processes can continue beyond an identifiable self-organizing phase, albeit with different time constants, thereby maintaining continuity and connectivity by maximizing the entropy-generation rate.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cond-mat.mtrl-sci'/>\n    <published>2026-03-03T22:15:50Z</published>\n    <arxiv:comment>42 pages and 13 Figures in the text and 3 Figures in the Appendix</arxiv:comment>\n    <arxiv:primary_category term='cond-mat.mtrl-sci'/>\n    <author>\n      <name>J. A. Sekhar</name>\n    </author>\n  </entry>"
}