2023/09/29 by Vidyesh Rao Anisetti, Anisetti, Vidyesh Rao, Ananth Kandala +3
Computer Science · Engineering · #Biological Physics (physics.bio-ph) #Data Visualization and Analytics #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Slime Mold and Myxomycetes Research #Soft Condensed Matter (cond-mat.soft) #Topological and Geometric Data Analysis
paper · pdf · doi:10.48550/arxiv.2309.16988
openalex publication_date 2023/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the realm of biological flow networks, the ability to dynamically adjust to varying demands is paramount. Drawing inspiration from the remarkable adaptability of Physarum polycephalum, we present a novel physical mechanism tailored to optimize flow networks. Central to our approach is the principle that each network component -- specifically, the tubes -- harnesses locally available information to collectively minimize a global cost function. Our findings underscore the scalability of this mechanism, making it feasible for larger, more complex networks. We construct a comprehensive phase diagram, pinpointing the specific network parameters under which successful adaptation, or tuning, is realized. There exists a phase boundary in the phase diagram, revealing a distinct satisfiability-unsatisfiability (SAT-UNSAT) phase transition delineating successful and unsuccessful adaptation.