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Related Concept Videos

Perception of Sound Waves01:01

Perception of Sound Waves

The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same frequency...
Electrostatic Boundary Conditions01:16

Electrostatic Boundary Conditions

Consider an external electric field propagating through a homogeneous medium. When the electric field crosses the surface boundary of the medium, it undergoes a discontinuity. The electric field can be resolved into normal and tangential components. The amount by which the field changes at any boundary is given by the difference between the field components above and below the surface boundary.
The surface integral of an electric field is given by Gauss's law in integral form and is related to...
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by identifying...
Sound as Pressure Waves01:17

Sound as Pressure Waves

Sound waves, which are longitudinal waves, can be modeled as the displacement amplitude varying as a function of the spatial and temporal coordinates. As a column of the medium is displaced, its successive columns are also displaced. As the successive displacements differ relatively, a pressure difference with the surrounding pressure is created. The gauge pressure varies across the medium.
The pressure fluctuation depends on the difference in displacements between the successive points in the...
Magnetostatic Boundary Conditions01:28

Magnetostatic Boundary Conditions

An electric field suffers a discontinuity at a surface charge. Similarly, a magnetic field is discontinuous at a surface current. The perpendicular component of a magnetic field is continuous across the interface of two magnetic mediums. In contrast, its parallel component, perpendicular to the current, is discontinuous by the amount equal to the product of the vacuum permeability and the surface current. Like the scalar potential in electrostatics, the vector potential is also continuous...
Wave Parameters01:10

Wave Parameters

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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Related Experiment Videos

Predicting acoustic field with a separate variable ocean physics-informed neural network.

Yong Ding1,2,3, Peng Xiao1,2,3, Bo Zhao1,2,3

  • 1School of Ocean Engineering and Technology, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China.

JASA Express Letters
|June 16, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel separable-variable OceanPINN for high-frequency underwater acoustics. It improves accuracy and generalization in acoustic field prediction, especially with limited data.

Related Experiment Videos

Area of Science:

  • Ocean acoustics
  • Computational physics
  • Machine learning

Background:

  • High-frequency underwater acoustic field modeling presents challenges for physics-informed neural networks (PINNs) due to strong oscillations.
  • Existing frameworks like OceanPINN struggle with the complexity of these acoustic fields.

Purpose of the Study:

  • To propose a novel separable-variable OceanPINN framework for enhanced 2D acoustic field prediction in high-frequency underwater environments.
  • To improve the accuracy and generalization capabilities of PINNs for acoustic modeling under data-scarce conditions.

Main Methods:

  • Developed a separable-variable OceanPINN utilizing learnable spectral expansions.
  • Represented the acoustic envelope using separable basis functions learned by 1D spectral neural networks.
  • Employed analytical derivatives of trigonometric bases for stable enforcement of the Helmholtz equation.

Main Results:

  • The proposed method demonstrated improved accuracy compared to conventional OceanPINN.
  • Enhanced generalization capabilities were observed, particularly under limited data conditions.
  • Efficient and stable enforcement of the Helmholtz equation was achieved.

Conclusions:

  • The separable-variable OceanPINN is effective for high-frequency underwater acoustic field modeling.
  • This approach offers a promising solution for accurate and generalizable acoustic predictions in challenging environments.
  • The method shows potential for applications where data is limited.