Aya dua harti ngeunaan watesan gerak Brown: hiji, dina fénoména fisik salaku gerak partikel dina fluida nu mangrupa gerak acak, sarta nu séjénna dina modél matematik nu dipaké pikeun ngajelaskeun hal éta.

Modél matematik bisa ogé digunakeun keur ngajelaskeun lobana fénoména séjén anu teu kasusun (ku matematik séjénna) ku gerak acak partikel. Nu biasa dipaké conto séjénna nyaéta turun unggahna pasar stok (Ing. stock market), sarta conto penting séjénna evolusi karakter fisik dina rekaman fosil.

Gerak Brown mangrupa prosés stokastik pangbasajanna dina domain kontinyu, and it is a limit of both simpler (see random walk) and more complicated stochastic processes. This universality is closely related to the universality of the normal distribution. In both cases, it is often mathematical convenience rather than actual accuracy as modéls that dictates their use. All three quoted examples of Brownian motion are cases of this: it has been argued that Lévy flights are a more accurate, if still imperfect, modél of stock-market fluctuations; the physical Brownian motion can be modélled more accurately by more general diffusion process; and the dust hasn't settled yet on what the best modél for the fossil 
record is, even after correcting for non-Gaussian data.

Sajarah gerak Brown 

Gerak Brown kapanggih ku ahli biologi Robert Brown taun 1827. The story goes that Brown was studying pollen particles floating in water under the 
microscope, and he observed minute particles within vacuoles in the pollen grains executing the jittery motion that now béars his name. By doing the same with particles of dust, he was able to rule out that the motion was due to pollen being "alive", but it remained to explain the origin of the motion. Nu pangheulana méré téori ngeunaan gerak Brown taya lian ti Albert Einstein taun 1905.

At that time the atomic nature of matter was still a controversial idéa. Einstein observed that, if the kinetic theory of 
fluids was right, then the molecules of water would move at random and so a small particle would receive a random 
number of impacts of random strength and from random directions in any short period of time. This random bombardment by 
the molecules of the fluid would cause a sufficiently small particle to move in exactly the way described by Brown.

Dadaran modél matematis 

Sacara matematik, gerak Brown mangrupa prosés Wiener nu mana sebaran kondisional probailiti tina posisi partikel dina waktu t+dt, nu dina posisi waktu t nyaéta p, mangrupa sebaran normal mibanda mean p+μ dt sarta varian σ2 dt; paraméter μ mangrupa simpangan kecepatan, sarta paraméter σ2 mangrupa power noise. These properties cléarly establish that Brownian motion is Markovian (i.e. it satisfies the Markov property). Brownian motion is related to the random walk problem and it is generic in the sense that many different stochastic processes reduce to Brownian motion in suitable limits.

In fact, the Wiener process is the only time-homogeneous stochastic process with independent increments and which is continuous in probability. These are all réasonable approximations to the physical properties of Brownian motion.

The mathematical théory of Brownian motion has been applied in contexts ranging far beyond the movement of particles in fluids. For example, in the modérn théory of option pricing, asset classes are sometimes modéled as if they move according to a Brownian motion with drift.

It turns out that the Wiener process is not a physically réalistic modél of the motion of Brownian particles. More sophisticated formulations of the problem have led to the mathematical théory of diffusion processes. The accompanying equation of motion is called the Langevin equation or the Fokker-Planck equation depending on whether it is formulated in terms of random trajectories or probability densities.

Tempo ogé 

osmosis, tangkal brown (Ing. brownian tree), ultramikroskop, Brownian ratchet

Tumbu kaluar 
 Edward Nelson, Dynamical Theories of Brownian Motion (1967) PDF of this out of print book available on the author's webpage.

Prosés stokastik