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Langevin equation

Langevin equation is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Langevin equation rather than just read about it. In short: In physics, a Langevin equation (named after Paul Langevin) is a stochastic differential equation describing how a system evolves when subjected to a combination of deterministic and fluctuating ("random") forces. The dependent variables in a Langevin equation typically are collective (macroscopic) variables changing only slowly in comparison to the other (microscopic) variables of the system.

Langevin equation — main illustration
Langevin equation — illustration

Key takeaways

  • Langevin equation belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Langevin equation to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Langevin equation from memory before moving on to harder problems.

Reference excerpt

In physics, a Langevin equation (named after Paul Langevin) is a stochastic differential equation describing how a system evolves when subjected to a combination of deterministic and fluctuating ("random") forces. The dependent variables in a Langevin equation typically are collective (macroscopic) variables changing only slowly in comparison to the other (microscopic) variables of the system. The fast (microscopic) variables are responsible for the stochastic nature of the Langevin equation. One application is to Brownian motion, which models the fluctuating motion of a small particle in a fluid.

Brownian motion as a prototype The original Langevin equation describes Brownian motion, the apparent random movement of a particle in a fluid due to collisions with the molecules of the fluid,

m d v d t = − λ v + η ( t ) , {\displaystyle m{\frac {\mathrm {d} \mathbf {v} }{\mathrm {d} t}}=-\lambda \mathbf {v} +{\boldsymbol {\eta }}(t),}

where v {\displaystyle \mathbf {v} } is the velocity of the particle, λ {\displaystyle \lambda } is its damping coefficient, and m {\displaystyle m} is its mass. The force acting on the particle is written as a sum of a viscous force proportional to the particle's velocity (Stokes' law), and a noise term η ( t ) {\displaystyle {\boldsymbol {\eta }}(t)} representing the effect of the collisions with the molecules of the fluid. The force η ( t ) {\displaystyle {\boldsymbol {\eta }}(t)} has a Gaussian probability distribution with correlation function

⟨ η i ( t ) η j ( t ′ ) ⟩ = 2 λ k B T δ i , j δ ( t − t ′ ) , {\displaystyle \langle \eta _{i}(t)\,\eta _{j}(t')\rangle =2\lambda k_{\text{B}}T\delta _{i,j}\delta (t-t'),}

where k B {\displaystyle k_{\text{B}}} is the Boltzmann constant, T {\displaystyle T} is the temperature, and η i ( t ) {\displaystyle \eta _{i}(t)} is the i-th component of the vector η ( t ) {\displaystyle {\boldsymbol {\eta }}(t)} . The δ {\displaystyle \delta } -function form of the time correlation means that the force at a time t {\displaystyle t} is uncorrelated with the force at any other time. This is an approximation: the actual random force has a nonzero correlation time corresponding to the collision time of the molecules. However, the Langevin equation is used to describe the motion of a "macroscopic" particle at a much longer time scale, and in this limit the δ {\displaystyle \delta } -correlation and the Langevin equation becomes virtually exact. This approximation of δ {\displaystyle \delta } -correlated (white) noise breaks down under certain conditions, especially in the presence of external driving forces that bring the system out of equilibrium, such as shear flow or for dense charged systems under AC external drive. This is also the case of the Langevin equation for relativistic systems, where the δ {\displaystyle \delta } -correlated noise assumption is in tension with the principle of locality. Another common feature of the Langevin equation is the occurrence of the damping coefficient λ {\displaystyle \lambda } in the correlation function of the random force, which in an equilibrium system is an expression of the Einstein relation.

Mathematical aspects A strictly δ {\displaystyle \delta } -correlated fluctuating force η ( t ) {\displaystyle {\boldsymbol {\eta }}(t)} is not a function in the usual mathematical sense, and even the derivative d v / d t {\displaystyle \mathrm {d} \mathbf {v} /\mathrm {d} t} is not defined in this limit. This problem disappears when the Langevin equation is written in integral form

… excerpt ends here. Continue reading the full article.

Illustrations

Langevin equation: Phase portrait of a harmonic oscillator showing spreading due to the Langevin equation
Phase portrait of a harmonic oscillator showing spreading due to the Langevin equation
Langevin equation: Equilibrium probability for Langevin dynamics in harmonic potential
Equilibrium probability for Langevin dynamics in harmonic potential
Langevin equation illustration
Langevin equation: Simulated squared displacements of free Brownian particles (semi-transparent wiggly lines) as a function of time, for three selected choices of initial squared velocity, which are 0, 3kBT/m, and 6kBT/m respectively, with 3kBT/m being the equipartition value in thermal equilibrium. The colored solid curves denote the mean squared displacements for the corresponding parameter choices.
Simulated squared displacements of free Brownian particles (semi-transparent wiggly lines) as a function of time, for three selected choices of initial squared velocity, which are 0, 3kBT/m, and 6kBT/m respectively, with 3kBT/m being the equipartition value in thermal equilibrium. The colored solid curves denote the mean squared displacements for the corresponding parameter choices.

Worked examples

Example 1 — a first encounter with Langevin equation

Start with the simplest possible case. Write down what Langevin equation claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Langevin equation before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Langevin equation ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Langevin equation

In research
Langevin equation appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Langevin equation in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Langevin equation is common in secondary-school and first-year university syllabi. It links to neighbouring topics Statistical mechanics, Stochastic differential equations, so understanding it makes those chapters shorter.
In everyday life
Look for Langevin equation outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Langevin equation in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Langevin equation means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Langevin equation out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Langevin equation in simple terms?

In physics, a Langevin equation (named after Paul Langevin) is a stochastic differential equation describing how a system evolves when subjected to a combination of deterministic and fluctuating ("random") forces. The dependent variables in a Langevin equation typically are collective (macroscopic)…

Why does Langevin equation matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Langevin equation?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Langevin equation.

Tags

  • Statistical mechanics
  • Stochastic differential equations

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