Toward neuromorphic computing using longitudinal pulses in a fluid near phase transition

Matan Mussel, Giulia Marcucci

Research output: Contribution to journalArticlepeer-review

Abstract

Longitudinal waves propagate information about the stimulus in multiple dimensions, including the medium density and pressure. Pulses that reversibly cross a phase transition have a nonlinear response that resembles properties of neuronal signaling. This multidimensionality suggests that longitudinal pulses may be harnessed for in-materio computation, mimicking biological or artificial neural algorithms. To explore a feedforward physical neural network using longitudinal pulses, we demonstrate the implementation of (1) a complete set of logic gates, (2) classification of data, and (3) regression of a mathematical function. Our results illustrate the potential of harnessing nonlinear longitudinal waves—common in a plethora of materials—for the purpose of computation.

Original languageEnglish
Article number046111
JournalPhysics of Fluids
Volume36
Issue number4
DOIs
StatePublished - 1 Apr 2024

Bibliographical note

Publisher Copyright:
© 2024 Author(s).

ASJC Scopus subject areas

  • Computational Mechanics
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

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