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Related Concept Videos

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the problem,...
The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra. Schrödinger...

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Related Experiment Videos

Solving the nonlinear Schrodinger equation with an unsupervised neural network.

C Monterola, C Saloma

    Optics Express
    |May 8, 2009
    PubMed
    Summary

    We developed an unsupervised neural network (NN) to solve the nonlinear Schrodinger equation. This method accurately solves complex differential equations, offering a universal approach for scientific computing.

    Area of Science:

    • Computational physics
    • Applied mathematics
    • Machine learning

    Background:

    • Solving complex differential equations like the nonlinear Schrodinger equation is crucial in physics and engineering.
    • Traditional numerical methods can be computationally intensive and may face challenges with complex boundary conditions.

    Purpose of the Study:

    • To introduce and validate an unsupervised neural network (NN) approach for solving the nonlinear Schrodinger equation.
    • To demonstrate the generalizability and accuracy of the NN method for complex differential equations.

    Main Methods:

    • Utilized an unsupervised neural network with optical axis position (z) and time (t) as inputs.
    • Trained the network by minimizing a non-negative energy function derived from the equation and boundary conditions.

    Related Experiment Videos

  • Assessed the network's generalization capability on unseen (z, t) combinations.
  • Main Results:

    • Achieved normalized mean-squared errors of approximately 10^-2.
    • Reduced the average energy from 10^-4 to 10^-2 during training.
    • Demonstrated that the trained network can accurately predict solutions for any (z, t) pair.

    Conclusions:

    • The unsupervised NN method provides an accurate and universal approach for solving the nonlinear Schrodinger equation.
    • This technique shows potential for application to a wide range of complex differential equations.
    • The NN method offers a powerful alternative to traditional numerical solvers in scientific computing.