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相关概念视频

Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

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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...
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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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
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The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

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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.
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Ampere's Law: Problem-Solving01:31

Ampere's Law: Problem-Solving

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Ampere's law states that for any closed looped path, the line integral of the magnetic field along the path equals the vacuum permeability times the current enclosed in the loop. If the fingers of the right hand curl along the direction of the integration path, the current in the direction of the thumb is considered positive. The current opposite to the thumb direction is considered negative.
Specific steps need to be considered while calculating the symmetric magnetic field distribution...
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Biot-Savart Law: Problem-Solving00:59

Biot-Savart Law: Problem-Solving

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The magnitude and direction of a magnetic field created by a steady current can be calculated using the Biot-Savart law.
Consider a mobile phone battery bank as a source of steady current, which flows through the wire connected between the two. What is the magnitude of the magnetic field created by this current at a field point P?
To estimate the magnitude of the total magnetic field, we first consider a small current element of length dl, at a distance r from the field point. Now the following...
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用人工神经网络解决量子多体问题

Giuseppe Carleo1, Matthias Troyer2,3

  • 1Theoretical Physics, ETH Zurich, 8093 Zurich, Switzerland. gcarleo@ethz.ch.

Science (New York, N.Y.)
|February 11, 2017
PubMed
概括

机器学习通过学习波函数来简化量子多体问题. 这种方法准确地描述了复杂的量子系统,包括交互自旋模型.

科学领域:

  • 量子物理
  • 计算物理
  • 机器学习

背景情况:

  • 量子多体问题由于波函数的指数复杂性而计算密集.
  • 在量子系统中描述非碎的相关性仍然是一个重大挑战.

研究的目的:

  • 证明机器学习能够减少量子多体问题的复杂性.
  • 通过人工神经网络引入量子状态的新型变量表示.
  • 提出一种强化学习方案,用于寻找基本状态并模拟时间演变.

主要方法:

  • 使用可变数量的隐藏神经元进行变化表示的人工神经网络.
  • 实施强化学习计划来训练神经网络.
  • 将该方法应用于一维和二维的原型交互旋转模型.

主要成果:

  • 机器学习系统地降低了多体波函数的复杂性.
  • 强化学习计划成功地识别了基本状态.
  • 这种方法准确地描述了复杂的相互作用量子系统的单元时间演变.
  • 在一维和二维交互旋转模型中实现了高精度.

结论:

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  • 机器学习为量子多体问题提供了可处理的计算方法.
  • 拟议的神经网络表示和强化学习方案对量子系统模拟有效.
  • 这种方法有望解决复杂的量子物理挑战.