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Related Concept Videos

Propagation of Waves01:07

Propagation of Waves

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When a wave propagates from one medium to another, part of it may get reflected in the first medium, and part of it may get transmitted to the second medium. In such a case, the interface of the two mediums can be considered as a boundary that is neither fixed nor free.
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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
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Wave Parameters01:10

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The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
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Sound Waves01:01

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Mathematically, the motion of a wave can be studied using a wavefunction. Consider a string oscillating up and down in simple harmonic motion, having a period T. The wave on the string is sinusoidal and is translated in the positive x-direction as time progresses. Sine is a function of the angle θ, oscillating between +A and −A and repeating every 2π radians. To construct a wave model, the ratio of the angle θ and the position x is considered.
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Related Experiment Video

Updated: Dec 31, 2025

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

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Wave physics as an analog recurrent neural network.

Tyler W Hughes1, Ian A D Williamson2, Momchil Minkov2

  • 1Department of Applied Physics, Stanford University, Stanford, CA 94305, USA.

Science Advances
|January 7, 2020
PubMed
Summary

Analog hardware using wave physics can be trained for machine learning tasks. This approach offers a faster, more energy-efficient alternative to digital systems for processing time-varying signals.

Related Experiment Videos

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Area of Science:

  • Physics
  • Machine Learning
  • Signal Processing

Background:

  • Analog hardware offers potential speed and energy efficiency advantages over digital systems for machine learning.
  • Wave physics, encompassing acoustics and optics, provides a natural framework for processing time-varying signals.

Purpose of the Study:

  • To establish a connection between wave physics dynamics and recurrent neural network computations.
  • To demonstrate that physical wave systems can be trained for complex temporal data analysis.
  • To explore analog machine learning platforms for efficient information processing.

Main Methods:

  • Identifying a computational mapping between wave physics and recurrent neural networks.
  • Utilizing standard neural network training techniques to train physical wave systems.
  • Designing an inhomogeneous medium using inverse design principles.

Main Results:

  • Demonstrated that an inverse-designed inhomogeneous medium can classify vowel sounds from raw audio signals.
  • Achieved vowel classification performance comparable to digital recurrent neural network implementations.
  • Showcased the ability of wave propagation through a physical medium to perform machine learning tasks.

Conclusions:

  • Physical wave systems can be trained to learn complex features in temporal data.
  • Analog machine learning platforms based on wave physics are feasible and efficient.
  • This research opens avenues for novel, high-performance analog computing hardware.