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Related Concept Videos

Magnetostatic Boundary Conditions01:28

Magnetostatic Boundary Conditions

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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...
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Electromagnetic Fields01:30

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Electric fields generated by static charges, often referred to as electrostatic fields, are characteristically different from electric fields created by time-varying magnetic fields. While the former is a conservative field, implying that no net work is done on a test charge if it goes around in a complete loop in the field, the latter is, by definition, not a conservative field; net work is done, and it is proportional to the rate of change of magnetic flux.
However, the observation of...
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Induced Electric Fields: Applications01:27

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An important distinction exists between the electric field induced by a changing magnetic field and the electrostatic field produced by a fixed charge distribution. Specifically, the induced electric field is nonconservative because it does not work in moving a charge over a closed path. In contrast, the electrostatic field is conservative and does no net work over a closed path. Hence, electric potential can be associated with the electrostatic field but not the induced field. The following...
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Equipotential Surfaces and Field Lines01:29

Equipotential Surfaces and Field Lines

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Electric potential can be pictorially represented as a three-dimensional surface. On such a surface, the electric potential is constant everywhere. The equipotential surface is always perpendicular to the electric field lines, and while it is three-dimensional, it can be treated as an equipotential line in a two-dimensional case. These equipotential lines are also always perpendicular to electric field lines. The term equipotential is often used as a noun, referring to an equipotential line or...
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Induced Electric Fields01:23

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The fact that emfs are induced in circuits implies that work is being done on the conduction electrons in the wires. What can possibly be the source of this work? We know that it’s neither a battery nor a magnetic field, as a battery does not have to be present in a circuit where current is induced, and magnetic fields never do any work on moving charges. The source of the work is in fact an electric field that is induced in the wires. For example, if a stationary conductor is placed in a...
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Electric Field of a Non Uniformly Charged Sphere01:22

Electric Field of a Non Uniformly Charged Sphere

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Gauss's law states that the electric flux through any closed surface equals the net charge enclosed within the surface. This law is beneficial for determining the expressions for the electric field for a particular charge distribution if the electric flux is known.
Consider a non-uniformly charged sphere, for which the density of charge depends only on the distance from a point in space and not on the direction. Such a sphere has a spherically symmetrical charge distribution. Here, the electric...
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Updated: Jun 11, 2025

Electric-Field-Induced Neural Precursor Cell Differentiation in Microfluidic Devices
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NeMF: Neural Microphysics Fields.

Inbal Kom Betzer, Roi Ronen, Vadim Holodovsky

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    This study introduces the neural microphysics field (NeMF), a deep learning model for 3D cloud microphysics recovery from polarization images. NeMF enables detailed characterization of cloud properties, improving climate and weather predictions.

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    Area of Science:

    • Scientific imaging
    • Atmospheric science
    • Computational physics

    Background:

    • Inverse problems in scientific imaging aim to characterize heterogeneous materials using physical quantities like microphysics.
    • Accurate 3D microphysics of clouds are crucial for understanding cloud dynamics, lifetime, albedo, and their impact on Earth's energy balance and rainfall.
    • Existing methods provide limited representations of cloud microphysics.

    Purpose of the Study:

    • To develop a novel method for 3D volumetric recovery of cloud microphysical parameters.
    • To introduce the neural microphysics field (NeMF) for enhanced characterization of cloud properties.
    • To improve the accuracy and detail of microphysical retrievals from multi-view polarization images.

    Main Methods:

    • A deep neural network, NeMF, is employed, taking multi-view polarization images as input.
    • NeMF is pre-trained using supervised learning, incorporating polarized radiative transfer and noise modeling for polarization-sensitive sensors.
    • The method focuses on recovering microphysical parameters, including droplet effective variance.

    Main Results:

    • NeMF achieves unprecedented recovery of 3D cloud microphysical parameters.
    • The model demonstrates robust performance in rigorous simulations.
    • Successful application to real-world polarization-image data validates its effectiveness.

    Conclusions:

    • NeMF offers a significant advancement in retrieving detailed 3D cloud microphysics.
    • This technology has the potential to improve climate modeling and weather forecasting.
    • The method provides a powerful tool for analyzing cloud properties from observational data.