FEEN: A Unified Theory of Phononic Mesh Computing — APPLICATIONS

This preprint presents the application landscape for FEEN (Frequency‑Encoded Elastic Networks), a phononic computing substrate that performs computation through the dynamics of coupled mechanical resonators rather than through clocked digital logic. FEEN operates in the sub‑100 Hz to low‑kHz regime, encoding information in amplitude, phase, and frequency of sustained oscillations, with computation emerging from nonlinear coupled‑mode dynamics, fading‑memory behavior, and Kuramoto‑type synchronization. Unlike CMOS systems, FEEN requires no global clock, no high‑rate sampling, and no analog‑to‑digital conversion for signals in its native mechanical bandwidth. The paper identifies four primary application domains where FEEN’s physical properties provide concrete engineering advantages: (1) physical reservoir computing for temporal pattern recognition using nonlinear dynamics and Q‑factor‑controlled memory; (2) sensorimotor control for autonomous robotics, leveraging emergent phase locking for central pattern generator (CPG) behavior; (3) structural health monitoring, where non‑Markovian relaxation kernels enable continuous damage‑state estimation; and (4) clockless distributed synchronization, enabling GPS‑free phase coherence in sensor arrays and fractionated architectures. For each domain, the paper defines a realistic prototype target compatible with current MEMS fabrication capabilities and outlines the physical mechanism that enables FEEN to outperform conventional digital approaches. The work does not claim deployment readiness. Instead, it provides a physics‑grounded justification for each application, identifies engineering constraints such as the ~100 Hz effective clock ceiling and fabrication‑induced frequency dispersion, and defines falsifiable criteria for near‑term experimental validation. Together with the companion theoretical paper, this preprint establishes FEEN as a viable substrate for continuous‑time, low‑power, distributed dynamical computation and outlines a clear experimental roadmap toward hardware demonstration.