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Brownian motion
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This article is about Brownian motion as a natural phenomenon. For the stochastic process, see Wiener process. For Temperature, see Thermodynamic temperature. For internal energy, see Equipartition Theorem. For the mobility model, see Random walk.
2 dimensional random walk of a silver adatom on a Ag(111) surface [1]
This is a simulation of the Brownian motion of 5 particles (yellow) that collide with a large set of 800 particles. The yellow particles leave 5 blue trails of random motion and one of them has a red velocity vector.
This is a simulation of the Brownian motion of a big particle (dust particle) that collides with a large set of smaller particles (molecules of a gas) which move with different velocities in different random directions.
Brownian motion or pedesis (from Ancient Greek: πήδησις /pέːdεːsis/ "leaping") is the random motion of particles suspended in a fluid (a liquid or a gas) resulting from their collision with the fast-moving molecules in the fluid.[2]
This pattern of motion typically alternates random fluctuations in a particle's position inside a fluid sub-domain with a relocation to another sub-domain. Each relocation is followed by more fluctuations within the new closed volume. This pattern describes a fluid at thermal equilibrium, defined by a given temperature. Within such fluid there exists no preferential direction of flow as in transport phenomena. More specifically the fluid's overall linear and angular momenta remain null over time. It is important also to note that the kinetic energies of the molecular Brownian motions, together with those of molecular rotations and vibrations sum up to the caloric component of a fluid’s internal energy.
This motion is named after the botanist Robert Brown, who was the most eminent microscopist of his time. In 1827, while looking through a microscope at pollen of the plant Clarkia Pulchella immersed in water, the triangular shaped pollen burst at the corners, emitting particles which he noted jiggled around in the water in random fashion. He was not able to determine the mechanisms that caused this motion. Atoms and molecules had long been theorized as the constituents of matter, and Albert Einstein published a paper in 1905 that explained in precise detail how the motion that Brown had observed was a result of the pollen being moved by individual water molecules, making one of his first big contributions to science. This explanation of Brownian motion served as convincing evidence that atoms and molecules exist, and was further verified experimentally by Jean Perrin in 1908. Perrin was awarded the Nobel Prize in Physics in 1926 "for his work on the discontinuous structure of matter". The direction of the force of atomic bombardment is constantly changing, and at different times the particle is hit more on one side than another, leading to the seemingly random nature of the motion.
The many-body interactions that yield the Brownian pattern cannot be solved by a model accounting for every involved molecule. In consequence only probabilistic models applied to molecular populations can be employed to describe it. Two such models of the statistical mechanics, due to Einstein and Smoluchowski are presented below. Another, pure probabilistic class of models is the class of the stochastic process models. There exist both simpler and more complicated stochastic processes which in extreme ("taken to the limit") may describe the Brownian Motion (see random walk and Donsker's theorem).
Contents
1 History
2 Statistical mechanics theories
2.1 Einstein's theory
2.2 Smoluchowski model
2.3 Other physics models using partial differential equations
2.4 Astrophysics: star motion within galaxies
3 Mathematics
3.1 Statistics
3.2 Lévy characterisation
3.3 Riemannian manifold
4 Narrow escape
5 See also
6 References
7 Further reading
8 External links
History[edit]
The Roman Lucretius's scientific poem "On the Nature of Things" (c. 60 BC) has a remarkable description of Brownian motion of dust particles in verses 113–140 from Book II. He uses this as a proof of the existence of atoms:
Reproduced from the book of Jean Baptiste Perrin, Les Atomes, three tracings of the motion of colloidal particles of radius 0.53 µm, as seen under the microscope, are displayed. Successive positions every 30 seconds are joined by straight line segments (the mesh size is 3.2 µm).[3]
"Observe what happens when sunbeams are admitted into a building and shed light on its shadowy places. You will see a multitude of tiny particles mingling in a multitude of ways... their dancing is an actual indication of underlying movements of matter that are hidden from our sight... It originates with the atoms which move of themselves [i.e., spontaneously]. Then those small compound bodies that are least removed from the impetus of the atoms are set in motion by the impact of their invisible blows and in turn cannon against slightly larger bodies. So the movement mounts up from the atoms and gradually emerges to the level of our senses, so that those bodies are in motion that we see in sunbeams, moved by blows that remain invisible."
Although the mingling motion of dust particles is caused largely by air currents, the glittering, tumbling motion of small dust particles is, indeed, caused chiefly by true Brownian dynamics.
While Jan Ingenhousz described the irregular motion of coal dust particles on the surface of alcohol in 1785, the discovery of this phenomenon is often credited to the botanist Robert Brown in 1827. Brown was studying pollen grains of the plant Clarkia pulchella suspended in water under a microscope when he observed minute particles, ejected by the pollen grains, executing a jittery motion. By repeating the experiment with particles of inorganic matter he was able to rule out that the motion was life-related, although its origin was yet to be explained.
The first person to describe the mathematics behind Brownian motion was Thorvald N. Thiele in a paper on the method of least squares published in 1880. This was followed independently by Louis Bachelier in 1900 in his PhD thesis "The theory of speculation", in which he presented a stochastic analysis of the stock and option markets. The Brownian motion model of the stock market is often cited, but Benoit Mandelbrot rejected its applicability to stock price movements in part because these are discontinuous.[4]
Albert Einstein (in one of his 1905 papers) and Marian Smoluchowski (1906) brought the solution of the problem to the attention of physicists, and presented it as a way to indirectly confirm the existence of atoms and molecules. Their equations describing Brownian motion were subsequently verified by the experimental work of Jean Baptiste Perrin in 1908.
Statistical mechanics theories[edit]
Einstein's theory[edit]
There are two parts to Einstein's theory: the first part consists in the formulation of a diffusion equation for Brownian particles, in which the diffusion coefficient is related to the mean squared displacement of a Brownian particle, while the second part consists in relating the diffusion coefficient to measurable physical quantities.[5] In this way Einstein was able to determine the size of atoms, and how many atoms there are in a mole, or the molecular weight in grams, of a gas.[6] In accordance to Avogadro's law this volume is the same for all ideal gases, which is 22.414 liters at standard temperature and pressure. The number of atoms contained in this volume is referred to as Avogadro's constant, and the determination of this number is tantamount to the knowledge of the mass of an atom since the latter is obtained by dividing the mass of a mole of the gas by Avogadro's constant.
The characteristic bell-shaped curves of the diffusion of Brownian particles. The distribution begins as a Dirac delta function, indicating that all the particles are located at the origin at time t=0, and for increasing times they become flatter and flatter until the distribution becomes uniform in the asymptotic time limit.
The first part of Einstein's argument was to determine how far a Brownian particle travels in a given time interval.[7][citation needed] Classical mechanics is unable to determine this distance because of the enormous number of bombardments a Brownian particle will undergo, roughly of the order of 1014 collisions per second.[2] Thus Einstein was led to consider the collective motion of Brownian particles.[citation needed]
He regarded the increment of particle positions in time τ {\displaystyle \tau } in a one dimensional (x) space (with the coordinates chosen so that the origin lies at the initial position of the particle) as a random variable ( Δ {\displaystyle \Delta } ) with some probability density function φ ( Δ ) {\displaystyle \varphi (\Delta )} . Further, assuming conservation of particle number, he expanded the density (number of particles per unit volume) at time t + τ {\displaystyle t+\tau } in a Taylor series,
ρ ( x , t ) + τ ∂ ρ ( x ) ∂ t + ⋯ = ρ ( x , t + τ ) = ∫ − ∞ + ∞ ρ ( x − Δ , t ) ⋅ φ ( Δ ) d Δ = E Δ [ ρ ( x , t + τ ) ] = ρ ( x , t ) ⋅ ∫ − ∞ + ∞ φ ( Δ ) d Δ − ∂ ρ ∂ x ⋅ ∫ − ∞ + ∞ Δ ⋅ φ ( Δ ) d Δ + ∂ 2 ρ ∂ x 2 ⋅ ∫ − ∞ + ∞ Δ 2 2 ⋅ φ ( Δ ) d Δ + ⋯ = ρ ( x , t ) ⋅ 1 + 0 + ∂ 2 ρ ∂ x 2 ⋅ ∫ − ∞ + ∞ Δ 2 2 ⋅ φ ( Δ ) d Δ + ⋯ {\displaystyle {\begin{aligned}\rho (x,t)+\tau {\frac {\partial \rho (x)}{\partial t}}+\cdots =\rho (x,t+\tau )={}&\int _{-\infty }^{+\infty }\rho (x-\Delta ,t)\cdot \varphi (\Delta )\,\mathrm {d} \Delta =\mathbb {E} _{\Delta }[\rho (x,t+\tau )]\\={}&\rho (x,t)\cdot \int _{-\infty }^{+\infty }\varphi (\Delta )\,d\Delta -{\frac {\partial \rho }{\partial x}}\cdot \int _{-\infty }^{+\infty }\Delta \cdot \varphi (\Delta )\,\mathrm {d} \Delta \\&{}+{\frac {\partial ^{2}\rho }{\partial x^{2}}}\cdot \int _{-\infty }^{+\infty }{\frac {\Delta ^{2}}{2}}\cdot \varphi (\Delta )\,\mathrm {d} \Delta +\cdots \\={}&\rho (x,t)\cdot 1+0+{\frac {\partial ^{2}\rho }{\partial x^{2}}}\cdot \int _{-\infty }^{+\infty }{\frac {\Delta ^{2}}{2}}\cdot \varphi (\Delta )\,\mathrm {d} \Delta +\cdots \end{aligned}}}
where the second equality in the first line is by definition of φ {\displaystyle \varphi } . The integral in the first term is equal to one by the definition of probability, and the second and other even terms (i.e. first and other odd moments) vanish because of space symmetry. What is left gives rise to the following relation:
∂ ρ ∂ t = ∂ 2 ρ ∂ x 2 ⋅ ∫ − ∞ + ∞ Δ 2 2 τ ⋅ φ ( Δ ) d Δ + higher-order even moments {\displaystyle {\frac {\partial \rho }{\partial t}}={\frac {\partial ^{2}\rho }{\partial x^{2}}}\cdot \int _{-\infty }^{+\infty }{\frac {\Delta ^{2}}{2\,\tau }}\cdot \varphi (\Delta )\,\mathrm {d} \Delta +{\text{higher-order even moments}}}
Where the coefficient before the Laplacian, the second moment of probability of displacement Δ {\displaystyle \Delta } , is interpreted as mass diffusivity D:
D = ∫ − ∞ + ∞ Δ 2 2 τ ⋅ φ ( Δ ) d Δ {\displaystyle D=\int _{-\infty }^{+\infty }{\frac {\Delta ^{2}}{2\,\tau }}\cdot \varphi (\Delta )\,\mathrm {d} \Delta }
Then the density of Brownian particles ρ at point x at time t satisfies the diffusion equation:
∂ ρ ∂ t = D ⋅ ∂ 2 ρ ∂ x 2 , {\displaystyle {\frac {\partial \rho }{\partial t}}=D\cdot {\frac {\partial ^{2}\rho }{\partial x^{2}}},}
Assuming that N particles start from the origin at the initial time t=0, the diffusion equation has the solution
ρ ( x , t ) = N 4 π D t e − x 2 4 D t . {\displaystyle \rho (x,t)={\frac {N}{\sqrt {4\pi Dt}}}e^{-{\frac {x^{2}}{4Dt}}}.}
This expression (which is a normal distribution with the mean μ = 0 {\displaystyle \mu =0} and variance σ 2 = 2 D t {\displaystyle \sigma ^{2}=2Dt} usually called Brownian motion B t {\displaystyle B_{t}} ) allowed Einstein to calculate the moments directly. The first moment is seen to vanish, meaning that the Brownian particle is equally likely to move to the left as it is to move to the right. The second moment is, however, non-vanishing, being given by
x 2 ¯ = 2 D t . {\displaystyle {\overline {x^{2}}}=2\,D\,t.}
This expresses the mean squared displacement in terms of the time elapsed and the diffusivity. From this expression Einstein argued that the displacement of a Brownian particle is not proportional to the elapsed time, but rather to its square root.[5] His argument is based on a conceptual switch from the "ensemble" of Brownian particles to the "single" Brownian particle: we can speak of the relative number of particles at a single instant just as well as of the time it takes a Brownian particle to reach a given point.[8]
The second part of Einstein's theory relates the diffusion constant to physically measurable quantities, such as the mean squared displacement of a particle in a given time interval. This result enables the experimental determination of Avogadro's number and therefore the size of molecules. Einstein analyzed a dynamic equilibrium being established between opposing forces. The beauty of his argument is that the final result does not depend upon which forces are involved in setting up the dynamic equilibrium.
In his original treatment, Einstein considered an osmotic pressure experiment, but the same conclusion can be reached in other ways.
Consider, for instance, particles suspended in a viscous fluid in a gravitational field. Gravity tends to make the particles settle, whereas diffusion acts to homogenize them, driving them into regions of smaller concentration. Under the action of gravity, a particle acquires a downward speed of v = μmg, where m is the mass of the particle, g is the acceleration due to gravity, and μ is the particle's mobility in the fluid. George Stokes had shown that the mobility for a spherical particle with radius r is μ = 1 6 π η r {\displaystyle \mu ={\tfrac {1}{6\pi \eta r}}} , where η is the dynamic viscosity of the fluid. In a state of dynamic equilibrium, and under the hypothesis of isothermal fluid, the particles are distributed according to the barometric distribution
ρ = ρ 0 e − m g h k B T , {\displaystyle \rho =\rho _{0}e^{-{\frac {mgh}{k_{B}T}}},}
where ρ−ρ0 is the difference in density of particles separated by a height difference of h, kB is Boltzmann's constant (namely, the ratio of the universal gas constant, R, to Avogadro's number, N), and T is the absolute temperature. Avogadro's number is to be determined.
The equilibrium distribution for particles of gamboge shows the tendency for granules to move to regions of lower concentration when affected by gravity.
Dynamic equilibrium is established because the more that particles are pulled down by gravity, the greater the tendency for the particles to migrate to regions of lower concentration. The flux is given by Fick's law,
J = − D d ρ d h , {\displaystyle J=-D{\frac {d\rho }{dh}},}
where J = ρv. Introducing the formula for ρ, we find that
v = D m g k B T . {\displaystyle v={\frac {Dmg}{k_{B}T}}.}
In a state of dynamical equilibrium, this speed must also be equal to v = μmg. Notice that both expressions for v are proportional to mg, reflecting that the derivation is independent of the type of forces considered. Similarly, one can derive an equivalent formula for identical charged particles of charge q in a uniform electric field of magnitude E, where mg is replaced with the electrostatic force qE. Equating these two expressions yields a formula for the diffusivity, independent of mg or qE or other such forces:
x 2 ¯ 2 t = D = μ k B T = μ R T N = R T 6 π η r N . {\displaystyle {\frac {\overline {x^{2}}}{2t}}=D=\mu k_{B}T={\frac {\mu RT}{N}}={\frac {RT}{6\pi \eta rN}}.}
Here the first equality follows from the first part of Einstein's theory, the third equality follows from the definition of Boltzmann's constant as kB = R / N, and the fourth equality follows from Stokes's formula for the mobility. By measuring the mean squared displacement over a time interval along with the universal gas constant R, the temperature T, the viscosity η, and the particle radius r, Avogadro's number N can be determined.
The type of dynamical equilibrium proposed by Einstein was not new. It had been pointed out previously by J. J. Thomson[9] in his series of lectures at Yale University in May 1903 that the dynamic equilibrium between the velocity generated by a concentration gradient given by Fick's law and the velocity due to the variation of the partial pressure caused when ions are set in motion "gives us a method of determining Avogadro's Constant which is independent of any hypothesis as to the shape or size of molecules, or of the way in which they act upon each other".[9]
An identical expression to Einstein's formula for the diffusion coefficient was also found by Walther Nernst in 1888[10] in which he expressed the diffusion coefficient as the ratio of the osmotic pressure to the ratio of the frictional force and the velocity to which it gives rise. The former was equated to the law of van 't Hoff while the latter was given by Stokes's law. He writes k ′ = p 0 / k {\displaystyle k'=p_{0}/k} for the diffusion coefficient k′, where p 0 {\displaystyle p_{0}} is the osmotic pressure and k is the ratio of the frictional force to the molecular viscosity which he assumes is given by Stokes's formula for the viscosity. Introducing the ideal gas law per unit volume for the osmotic pressure, the formula becomes identical to that of Einstein's.[11] The use of Stokes's law in Nernst's case, as well as in Einstein and Smoluchowski, is not strictly applicable since it does not apply to the case where the radius of the sphere is small in comparison with the mean free path.[12]
At first, the predictions of Einstein's formula were seemingly refuted by a series of experiments by Svedberg in 1906 and 1907, which gave displacements of the particles as 4 to 6 times the predicted value, and by Henri in 1908 who found displacements 3 times greater than Einstein's formula predicted.[13] But Einstein's predictions were finally confirmed in a series of experiments carried out by Chaudesaigues in 1908 and Perrin in 1909. The confirmation of Einstein's theory constituted empirical progress for the kinetic theory of heat. In essence, Einstein showed that the motion can be predicted directly from the kinetic model of thermal equilibrium. The importance of the theory lay in the fact that it confirmed the kinetic theory's account of the second law of thermodynamics as being an essentially statistical law.[14]
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Brownian motion model of the trajectory of a particle of dye in water.
Smoluchowski model[edit]
Smoluchowski's theory of Brownian motion[15] starts from the same premise as that of Einstein and derives the same probability distribution ρ(x, t) for the displacement of a Brownian particle along the x in time t. He therefore gets the same expression for the mean squared displacement: ( Δ x ) 2 ¯ {\displaystyle {\overline {(\Delta x)^{2}}}} . However, when he relates it to a particle of mass m moving at a velocity u which is the result of a frictional force governed by Stokes's law, he finds
( Δ x ) 2 ¯ = 2 D t = t 32 81 μ 2 π μ a = t 64 27 1 2 μ 2 3 π μ a , {\displaystyle {\overline {(\Delta x)^{2}}}=2Dt=t{\frac {32}{81}}{\frac {\mu ^{2}}{\pi \mu a}}=t{\frac {64}{27}}{\frac {{\frac {1}{2}}\mu ^{2}}{3\pi \mu a}},}
where μ is the viscosity coefficient, and a is the radius of the particle. Associating the kinetic energy μ 2 / 2 {\displaystyle \mu ^{2}/2} with the thermal energy RT/N, the expression for the mean squared displacement is 64/27 times that found by Einstein. The fraction 27/64 was commented on by Arnold Sommerfeld in his necrology on Smoluchowski: "The numerical coefficient of Einstein, which differs from Smoluchowski by 27/64 can only be put in doubt."[16]
Smoluchowski[17] attempts to answer the question of why a Brownian particle should be displaced by bombardments of smaller particles when the probabilities for striking it in the forward and rear directions are equal. If the probability of m gains and n − m losses follows a binomial distribution,
P m , n = ( n m ) 2 − n , {\displaystyle P_{m,n}={\binom {n}{m}}2^{-n},}
with equal a priori probabilities of 1/2, the mean total gain is
2 m − n ¯ = ∑ m = n 2 n ( 2 m − n ) P m , n = n n ! 2 n [ ( n 2 ) ! ] 2 . {\displaystyle {\overline {2m-n}}=\sum _{m={\frac {n}{2}}}^{n}(2m-n)P_{m,n}={\frac {nn!}{2^{n}\left[\left({\frac {n}{2}}\right)!\right]^{2}}}.}
If n is large enough so that Stirling's approximation can be used in the form
n ! ≈ ( n e ) n 2 π n , {\displaystyle n!\approx \left({\frac {n}{e}}\right)^{n}{\sqrt {2\pi n}},}
then the expected total gain will be[citation needed]
2 m − n ¯ ≈ 2 n π , {\displaystyle {\overline {2m-n}}\approx {\sqrt {\frac {2n}{\pi }}},}
showing that it increases as the square root of the total population.
Suppose that a Brownian particle of mass M is surrounded by lighter particles of mass m which are traveling at a speed u. Then, reasons Smoluchowski, in any collision between a surrounding and Brownian particles, the velocity transmitted to the latter will be mu/M. This ratio is of the order of 10−7 cm/s. But we also have to take into consideration that in a gas there will be more than 1016 collisions in a second, and even greater in a liquid where we expect that there will be 1020 collision in one second. Some of these collisions will tend to accelerate the Brownian particle; others will tend to decelerate it. If there is a mean excess of one kind of collision or the other to be of the order of 108 to 1010 collisions in one second, then velocity of the Brownian particle may be anywhere between 10 and 1000 cm/s. Thus, even though there are equal probabilities for forward and backward collisions there will be a net tendency to keep the Brownian particle in motion, just as the ballot theorem predicts.
These orders of magnitude are not exact because they don't take into consideration the velocity of the Brownian particle, U, which depends on the collisions that tend to accelerate and decelerate it. The larger U is, the greater will be the collisions that will retard it so that the velocity of a Brownian particle can never increase without limit. Could such a process occur, it would be tantamount to a perpetual motion of the second type. And since equipartition of energy applies, the kinetic energy of the Brownian particle, M U 2 / 2 {\displaystyle MU^{2}/2} , will be equal, on the average, to the kinetic energy of the surrounding fluid particle, m u 2 / 2 {\displaystyle mu^{2}/2} .
In 1906 Smoluchowski published a one-dimensional model to describe a particle undergoing Brownian motion.[18] The model assumes collisions with M ≫ m where M is the test particle's mass and m the mass of one of the individual particles composing the fluid. It is assumed that the particle collisions are confined to one dimension and that it is equally probable for the test particle to be hit from the left as from the right. It is also assumed that every collision always imparts the same magnitude of ΔV. If NR is the number of collisions from the right and NL the number of collisions from the left then after N collisions the particle's velocity will have changed by ΔV(2NR − N). The multiplicity is then simply given by:
( N N R ) = N ! N R ! ( N − N R ) ! {\displaystyle {\binom {N}{N_{R}}}={\frac {N!}{N_{R}!(N-N_{R})!}}}
and the total number of possible states is given by 2N. Therefore, the probability of the particle being hit from the right NR times is:
P N ( N R ) = N ! 2 N N R ! ( N − N R ) ! {\displaystyle P_{N}(N_{R})={\frac {N!}{2^{N}N_{R}!(N-N_{R})!}}}
As a result of its simplicity, Smoluchowski's 1D model can only qualitatively describe Brownian motion. For a realistic particle undergoing Brownian motion in a fluid many of the assumptions cannot be made. For example, the assumption that on average there occurs an equal number of collisions from the right as from the left falls apart once the particle is in motion. Also, there would be a distribution of different possible ΔVs instead of always just one in a realistic situation.
Other physics models using partial differential equations[edit]
The diffusion equation yields an approximation of the time evolution of the probability density function associated to the position of the particle going under a Brownian movement under the physical definition. The approximation is valid on short timescales.
The time evolution of the position of the Brownian particle itself is best described using Langevin equation, an equation which involves a random force field representing the effect of the thermal fluctuations of the solvent on the particle.
The displacement of a particle undergoing Brownian motion is obtained by solving the diffusion equation under appropriate boundary conditions and finding the rms of the solution. This shows that the displacement varies as the square root of the time (not linearly), which explains why previous experimental results concerning the velocity of Brownian particles gave nonsensical results. A linear time dependence was incorrectly assumed.
At very short time scales, however, the motion of a particle is dominated by its inertia and its displacement will be linearly dependent on time: Δx = vΔt. So the instantaneous velocity of the Brownian motion can be measured as v = Δx/Δt, when Δt << τ, where τ is the momentum relaxation time. In 2010, the instantaneous velocity of a Brownian particle (a glass microsphere trapped in air with an optical tweezer) was measured successfully.[19] The velocity data verified the Maxwell–Boltzmann velocity distribution, and the equipartition theorem for a Brownian particle.
Astrophysics: star motion within galaxies[edit]
In stellar dynamics, a massive body (star, black hole, etc.) can experience Brownian motion as it responds to gravitational forces from surrounding stars.[20] The rms velocity V of the massive object, of mass M, is related to the rms velocity v ⋆ {\displaystyle v_{\star }} of the background stars by
M V 2 ≈ m v ⋆ 2 {\displaystyle MV^{2}\approx mv_{\star }^{2}}
where m ≪ M {\displaystyle m\ll M} is the mass of the background stars. The gravitational force from the massive object causes nearby stars to move faster than they otherwise would, increasing both v ⋆ {\displaystyle v_{\star }} and V.[20] The Brownian velocity of Sgr A*, the supermassive black hole at the center of the Milky Way galaxy, is predicted from this formula to be less than 1 km s−1.[21]
Mathematics[edit]
Main article: Wiener process
Play media
An animated example of a Brownian motion-like random walk on a torus. In the scaling limit, random walk approaches the Wiener process according to Donsker's theorem.
In mathematics, Brownian motion is described by the Wiener process; a continuous-time stochastic process named in honor of Norbert Wiener. It is one of the best known Lévy processes (càdlàg stochastic processes with stationary independent increments) and occurs frequently in pure and applied mathematics, economics and physics.
A single realisation of three-dimensional Brownian motion for times 0 ≤ t ≤ 2
The Wiener process Wt is characterized by four facts[citation needed]:
W0 = 0
Wt is almost surely continuous
Wt has independent increments
W t − W s ∼ N ( 0 , t − s ) {\displaystyle W_{t}-W_{s}\sim {\mathcal {N}}(0,t-s)} (for 0 ≤ s ≤ t {\displaystyle 0\leq s\leq t} ).
N ( μ , σ 2 ) {\displaystyle {\mathcal {N}}(\mu ,\sigma ^{2})} denotes the normal distribution with expected value μ and variance σ2. The condition that it has independent increments means that if 0 ≤ s 1 < t 1 ≤ s 2 < t 2 {\displaystyle 0\leq s_{1}<t_{1}\leq s_{2}<t_{2}} then W t 1 − W s 1 {\displaystyle W_{t_{1}}-W_{s_{1}}} and W t 2 − W s 2 {\displaystyle W_{t_{2}}-W_{s_{2}}} are independent random variables.
An alternative characterisation of the Wiener process is the so-called Lévy characterisation that says that the Wiener process is an almost surely continuous martingale with W0 = 0 and quadratic variation [ W t , W t ] = t {\displaystyle [W_{t},W_{t}]=t} .
A third characterisation is that the Wiener process has a spectral representation as a sine series whose coefficients are independent N ( 0 , 1 ) {\displaystyle {\mathcal {N}}(0,1)} random variables. This representation can be obtained using the Karhunen–Loève theorem.
The Wiener process can be constructed as the scaling limit of a random walk, or other discrete-time stochastic processes with stationary independent increments. This is known as Donsker's theorem. Like the random walk, the Wiener process is recurrent in one or two dimensions (meaning that it returns almost surely to any fixed neighborhood of the origin infinitely often) whereas it is not recurrent in dimensions three and higher. Unlike the random walk, it is scale invariant.
The time evolution of the position of the Brownian particle itself can be described approximately by a Langevin equation, an equation which involves a random force field representing the effect of the thermal fluctuations of the solvent on the Brownian particle. On long timescales, the mathematical Brownian motion is well described by a Langevin equation. On small timescales, inertial effects are prevalent in the Langevin equation. However the mathematical Brownian motion is exempt of such inertial effects. Note that inertial effects have to be considered in the Langevin equation, otherwise the equation becomes singular.[clarification needed] so that simply removing the inertia term from this equation would not yield an exact description, but rather a singular behavior in which the particle doesn't move at all.[clarification needed]
Statistics[edit]
The Brownian motion can be modeled by a random walk.[22] Random walks in porous media or fractals are anomalous.[23]
In the general case, Brownian motion is a non-Markov random process and described by stochastic integral equations.[24]
Lévy characterisation[edit]
The French mathematician Paul Lévy proved the following theorem, which gives a necessary and sufficient condition for a continuous Rn-valued stochastic process X to actually be n-dimensional Brownian motion. Hence, Lévy's condition can actually be used as an alternative definition of Brownian motion.
Let X = (X1, ..., Xn) be a continuous stochastic process on a probability space (Ω, Σ, P) taking values in Rn. Then the following are equivalent:
X is a Brownian motion with respect to P, i.e., the law of X with respect to P is the same as the law of an n-dimensional Brownian motion, i.e., the push-forward measure X∗(P) is classical Wiener measure on C0([0, +∞); Rn).
both
X is a martingale with respect to P (and its own natural filtration); and
for all 1 ≤ i, j ≤ n, Xi(t)Xj(t) −δijt is a martingale with respect to P (and its own natural filtration), where δij denotes the Kronecker delta.
Riemannian manifold[edit]
Brownian motion on a 2-sphere
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The infinitesimal generator (and hence characteristic operator) of a Brownian motion on Rn is easily calculated to be ½Δ, where Δ denotes the Laplace operator. In image processing and computer vision, the Laplacian operator has been used for various tasks such as blob and edge detection. This observation is useful in defining Brownian motion on an m-dimensional Riemannian manifold (M, g): a Brownian motion on M is defined to be a diffusion on M whose characteristic operator A {\displaystyle {\mathcal {A}}} in local coordinates xi, 1 ≤ i ≤ m, is given by ½ΔLB, where ΔLB is the Laplace–Beltrami operator given in local coordinates by
Δ L B = 1 det ( g ) ∑ i = 1 m ∂ ∂ x i ( det ( g ) ∑ j = 1 m g i j ∂ ∂ x j ) , {\displaystyle \Delta _{\mathrm {LB} }={\frac {1}{\sqrt {\det(g)}}}\sum _{i=1}^{m}{\frac {\partial }{\partial x_{i}}}\left({\sqrt {\det(g)}}\sum _{j=1}^{m}g^{ij}{\frac {\partial }{\partial x_{j}}}\right),}
where [gij] = [gij]−1 in the sense of the inverse of a square matrix.
Narrow escape[edit]
The narrow escape problem is a ubiquitous problem in biology, biophysics and cellular biology which has the following formulation: a Brownian particle (ion, molecule, or protein) is confined to a bounded domain (a compartment or a cell) by a reflecting boundary, except for a small window through which it can escape. The narrow escape problem is that of calculating the mean escape time. This time diverges as the window shrinks, thus rendering the calculation a singular perturbation problem.
See also[edit]
Brownian bridge: a Brownian motion that is required to "bridge" specified values at specified times
Brownian covariance
Brownian dynamics
Brownian motion of sol particles
Brownian motor
Brownian noise (Martin Gardner proposed this name for sound generated with random intervals. It is a pun on Brownian motion and white noise.)
Brownian ratchet
Brownian surface
Brownian tree
Brownian web
Rotational Brownian motion
Clinamen
Complex system
Continuity equation
Diffusion equation
Geometric Brownian motion
Itō diffusion: a generalisation of Brownian motion
Langevin equation
Lévy arcsine law
Local time (mathematics)
Many-body problem
Marangoni effect
Nanoparticle tracking analysis
Narrow escape problem
Osmosis
Random walk
Schramm–Loewner evolution
Single particle tracking
Statistical mechanics
Surface diffusion: a type of constrained Brownian motion.
Thermal equilibrium
Thermodynamic equilibrium
Tyndall effect: physical chemistry phenomenon where particles are involved; used to differentiate between the different types of mixtures.
Ultramicroscope
References[edit]
Jump up ^ Teaching the Growth, Ripening, and Agglomeration of Nanostructures in Computer Experiments, Jan Philipp Meyburg and Detlef Diesing, Journal of Chemical Education, (2017), 94, 9, 1225-1231, J.Chem.Edu
^ Jump up to: a b Feynman, R. (1964). "The Brownian Movement". The Feynman Lectures of Physics, Volume I. pp. 41&#45, 1.
Jump up ^ Perrin, Jean (1914). Atoms. p. 115.
Jump up ^ Mandelbrot, B.; Hudson, R. (2004). The (Mis)behavior of Markets: A Fractal View of Risk, Ruin, and Reward. Basic Books. ISBN 978-0-465-04355-2.
^ Jump up to: a b Einstein, Albert (1956) [1926]. Investigations on the Theory of the Brownian Movement (PDF). Dover Publications. Retrieved 2013-12-25.
Jump up ^ Stachel, J., ed. (1989). "Einstein's Dissertation on the Determination of Molecular Dimensions" (PDF). The Collected Papers of Albert Einstein, Volume 2. Princeton University Press.
Jump up ^ BROWNIAN MOTION. p. 5.
Jump up ^ Lavenda, Bernard H. (1985). Nonequilibrium Statistical Thermodynamics. John Wiley & Sons. p. 20. ISBN 978-0-471-90670-4.
^ Jump up to: a b Thomson, J. J. (1904). Electricity and Matter. Yale University Press. pp. 80–83.
Jump up ^ Nernst, Walther (1888). "Zur Kinetik der in Lösung befindlichen Körper". Zeitschrift für Physikalische Chemie (in German). 9: 613–637.
Jump up ^ Leveugle, J. (2004). La Relativité, Poincaré et Einstein, Planck, Hilbert. Harmattan. p. 181.
Jump up ^ Townsend, J.E.S. (1915). Electricity in Gases. Clarendon Press. p. 254.
Jump up ^ See P. Clark 1976, p. 97
Jump up ^ See P. Clark 1976 for this whole paragraph
Jump up ^ Smoluchowski, M. M. (1906). Sur le chemin moyen parcouru par les molécules d'un gaz et sur son rapport avec la théorie de la diffusion [On the average path taken by gas molecules and its relation with the theory of diffusion]. Bulletin International de l'Académie des Sciences de Cracovie (in French). p. 202.
Jump up ^ See p. 535 in Sommerfeld, A. (1917). "Zum Andenken an Marian von Smoluchowski" [In Memory of Marian von Smoluchowski]. Physikalische Zeitschrift (in German). 18 (22): 533–539.
Jump up ^ Smoluchowski, M. M. (1906). "Essai d'une théorie cinétique du mouvement Brownien et des milieux troubles" [Test of a kinetic theory of Brownian motion and turbid media]. Bulletin International de l'Académie des Sciences de Cracovie (in French): 577.
Jump up ^ von Smoluchowski, M. (1906). "Zur kinetischen Theorie der Brownschen Molekularbewegung und der Suspensionen". Annalen der Physik (in German). 326 (14): 756–780. Bibcode:1906AnP...326..756V. doi:10.1002/andp.19063261405.
Jump up ^ Li, Tongcang; Kheifets, Simon; Medellin, David; Raizen, Mark (2010). "Measurement of the instantaneous velocity of a Brownian particle" (PDF). Science. 328 (5986): 1673–1675. Bibcode:2010Sci...328.1673L. CiteSeerX 10.1.1.167.8245. doi:10.1126/science.1189403. PMID 20488989.
^ Jump up to: a b Merritt, David (2013). Dynamics and Evolution of Galactic Nuclei. Princeton University Press. p. 575. ISBN 9781400846122. OL 16802359W.
Jump up ^ Reid, M. J.; Brunthaler, A. (2004). "The Proper Motion of Sagittarius A*. II. The Mass of Sagittarius A*". The Astrophysical Journal. 616 (2): 872–884. arXiv:astro-ph/0408107. Bibcode:2004ApJ...616..872R. doi:10.1086/424960.
Jump up ^ Weiss, G. H. (1994). Aspects and applications of the random walk. North Holland.
Jump up ^ Ben-Avraham, D.; Havlin, S. (2000). Diffusion and reaction in disordered systems. Cambridge University Press.
Jump up ^ Morozov, A. N.; Skripkin, A. V. (2011). "Spherical particle Brownian motion in viscous medium as non-Markovian random process". Physics Letters A. 375 (46): 4113–4115. Bibcode:2011PhLA..375.4113M. doi:10.1016/j.physleta.2011.10.001.
Lester Eli Dubins & Gideon Schwarz (1965). On Continuous Martingales. Proceedings of the National Academy of Sciences.
Further reading[edit]
Brown, Robert (1828). "A brief account of microscopical observations made in the months of June, July and August, 1827, on the particles contained in the pollen of plants; and on the general existence of active molecules in organic and inorganic bodies" (PDF). Philosophical Magazine. 4: 161–173. Also includes a subsequent defense by Brown of his original observations, Additional remarks on active molecules.
Chaudesaigues, M. (1908). "Le mouvement brownien et la formule d'Einstein" [Brownian motion and Einstein's formula]. Comptes Rendus (in French). 147: 1044–6.
Clark, P. (1976). "Atomism versus thermodynamics". In Howson, Colin. Method and appraisal in the physical sciences. Cambridge University Press. ISBN 978-0521211109.
Cohen, Ruben D. (1986). "Self Similarity in Brownian Motion and Other Ergodic Phenomena" (PDF). Journal of Chemical Education. 63 (11): 933–934. Bibcode:1986JChEd..63..933C. doi:10.1021/ed063p933.
Einstein, A. (1905). "Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen" (PDF). Annalen der Physik (in German). 322 (8): 549–560. Bibcode:1905AnP...322..549E. doi:10.1002/andp.19053220806.
Einstein, A. (1956). Investigations on the Theory of Brownian Movement (PDF). New York: Dover. ISBN 978-0-486-60304-9.
Henri, V. (1908). "Études cinématographique du mouvement brownien" [Cinematographic studies of Brownian motion]. Comptes Rendus (in French) (146): 1024–6.
Lucretius, On The Nature of Things, translated by William Ellery Leonard. (on-line version, from Project Gutenberg. See the heading 'Atomic Motions'; this translation differs slightly from the one quoted).
Nelson, Edward, (1967). Dynamical Theories of Brownian Motion. (PDF version of this out-of-print book, from the author's webpage.) This is primarily a mathematical work, but the first four chapters discuss the history of the topic, in the era from Brown to Einstein.
Pearle, P.; Collett, B.; Bart, K.; Bilderback, D.; Newman, D.; Samuels, S. (2010). "What Brown saw and you can too". American Journal of Physics. 78 (12): 1278–1289. arXiv:1008.0039. Bibcode:2010AmJPh..78.1278P. doi:10.1119/1.3475685.
Perrin, J. (1909). "Mouvement brownien et réalité moléculaire" [Brownian movement and molecular reality]. Annales de chimie et de physique. 8th series. 18: 5–114.
See also Perrin's book "Les Atomes" (1914).
von Smoluchowski, M. (1906). "Zur kinetischen Theorie der Brownschen Molekularbewegung und der Suspensionen". Annalen der Physik. 21 (14): 756–780. Bibcode:1906AnP...326..756V. doi:10.1002/andp.19063261405.
Svedberg, T. (1907). Studien zur Lehre von den kolloiden Losungen.
Theile, T. N.
Danish version: "Om Anvendelse af mindste Kvadraters Methode i nogle Tilfælde, hvor en Komplikation af visse Slags uensartede tilfældige Fejlkilder giver Fejlene en ‘systematisk’ Karakter".
French version: "Sur la compensation de quelques erreurs quasi-systématiques par la méthodes de moindre carrés" published simultaneously in Vidensk. Selsk. Skr. 5. Rk., naturvid. og mat. Afd., 12:381–408, 1880.
External links[edit]
Wikimedia Commons has media related to Brownian motion.
Wikisource has original text related to this article:
A brief account of microscopical observations made on the particles contained in the pollen of plants
Brownian motion JavaScript simulation
Brownian motion java simulation
A single-molecule brownian motion diffusion simulator
Article for the school-going child
Einstein on Brownian Motion
Brownian Motion, "Diverse and Undulating"
Discusses history, botany and physics of Brown's original observations, with videos
"Einstein's prediction finally witnessed one century later" : a test to observe the velocity of Brownian motion
"Café math : brownian motion (Part 1)" : A blog article describing brownian motion (definition, symmetries, simulation)
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text:
List of current Indian chief ministers
From Wikipedia, the free encyclopedia
Jump to navigation Jump to search
Indian states according to party of their chief minister
Bharatiya Janata Party
Indian National Congress
Other parties
Governor's rule
In the Republic of India, a chief minister is the head of government of each of twenty-nine states and two union territories (Delhi and Puducherry). According to the Constitution of India, at the state-level, the governor is de jure head, but de facto executive authority rests with the chief minister. Following elections to the state legislative assembly, the governor usually invites the party (or coalition) with a majority of seats to form the government. The governor appoints the chief minister, whose council of ministers are collectively responsible to the assembly. Given he has the assembly's confidence, the chief minister's term is usually for a maximum of five years; there are no limits to the number of terms he/she can serve.[1]
Since June 2018, the office of Chief Minister of Jammu and Kashmir has been vacant; Governor's rule is in force there. Of the thirty incumbents, two are women—Mamata Banerjee in West Bengal and Vasundhara Raje in Rajasthan. Serving since December 1994 (for 23 years, 318 days), Sikkim's Pawan Kumar Chamling has the longest incumbency. Amarinder Singh (b. 1942) of Punjab is the oldest chief minister while Arunachal Pradesh's Pema Khandu (b. 1979) is the youngest.[2] Fifteen incumbents belong to the Bharatiya Janata Party and three to the Indian National Congress; no other party has more than one chief minister in office.
Current Indian chief ministers[edit]
Colour key for parties
Aam Aadmi Party
All India Anna Dravida Munnetra Kazhagam
All India Trinamool Congress
Bharatiya Janata Party
Biju Janata Dal
Communist Party of India (Marxist)
Indian National Congress
Janata Dal (Secular)
Janata Dal (United)
Nationalist Democratic Progressive Party
National People's Party
Sikkim Democratic Front
Telangana Rashtra Samithi
Telugu Desam Party
N/A (Governor's rule)
State
(past chief ministers)
Name[3]
Portrait
Took office
(tenure length)
Party[a]
Ref
Andhra Pradesh
(list) N. Chandrababu Naidu 8 June 2014
(7003160100000000000♠4 years, 140 days) Telugu Desam Party [4]
Arunachal Pradesh
(list) Pema Khandu 17 July 2016
(7002831000000000000♠2 years, 101 days) Bharatiya Janata Party [5][6]
Assam
(list) Sarbananda Sonowal 24 May 2016
(7002885000000000000♠2 years, 155 days) Bharatiya Janata Party [7]
Bihar
(list) Nitish Kumar 22 February 2015
(7003134200000000000♠3 years, 246 days) Janata Dal (United) [8]
Chhattisgarh
(list) Raman Singh 7 December 2003
(7003543700000000000♠14 years, 323 days) Bharatiya Janata Party [9]
Delhi[b]
(list) Arvind Kejriwal 14 February 2015
(7003135000000000000♠3 years, 254 days) Aam Aadmi Party [10]
Goa
(list) Manohar Parrikar 14 March 2017
(7002591000000000000♠1 year, 226 days) Bharatiya Janata Party [11]
Gujarat
(list) Vijay Rupani 7 August 2016
(7002810000000000000♠2 years, 80 days) Bharatiya Janata Party [12]
Haryana
(list) Manohar Lal Khattar 26 October 2014
(7003146100000000000♠4 years, 0 days) Bharatiya Janata Party [13]
Himachal Pradesh
(list) Jai Ram Thakur 27 December 2017
(7002303000000000000♠303 days) Bharatiya Janata Party [14]
Jammu and Kashmir
(list) Vacant[c]
(Governor's rule) 20 June 2018
(7002128000000000000♠128 days) N/A [16]
Jharkhand
(list) Raghubar Das 28 December 2014
(7003139800000000000♠3 years, 302 days) Bharatiya Janata Party [17]
Karnataka
(list) H. D. Kumaraswamy 23 May 2018
(7002156000000000000♠156 days) Janata Dal (Secular) [18]
Kerala
(list) Pinarayi Vijayan 25 May 2016
(7002884000000000000♠2 years, 154 days) Communist Party of India (Marxist) [19]
Madhya Pradesh
(list) Shivraj Singh Chouhan 29 November 2005
(7003471400000000000♠12 years, 331 days) Bharatiya Janata Party [20]
Maharashtra
(list) Devendra Fadnavis 31 October 2014
(7003145600000000000♠3 years, 360 days) Bharatiya Janata Party [21]
Manipur
(list) N. Biren Singh 15 March 2017
(7002590000000000000♠1 year, 225 days) Bharatiya Janata Party [22]
Meghalaya
(list) Conrad Sangma 6 March 2018
(7002234000000000000♠234 days) National People's Party [23]
Mizoram
(list) Lal Thanhawla 7 December 2008
(7003361000000000000♠9 years, 323 days) Indian National Congress [24]
Nagaland
(list) Neiphiu Rio 8 March 2018
(7002232000000000000♠232 days) Nationalist Democratic Progressive Party [25]
Odisha
(list) Naveen Patnaik 5 March 2000
(7003680900000000000♠18 years, 235 days) Biju Janata Dal [26]
Puducherry[b]
(list) V. Narayanasamy 6 June 2016
(7002872000000000000♠2 years, 142 days) Indian National Congress [27]
Punjab
(list) Amarinder Singh 16 March 2017
(7002589000000000000♠1 year, 224 days) Indian National Congress [28]
Rajasthan
(list) Vasundhara Raje 13 December 2013
(7003177800000000000♠4 years, 317 days) Bharatiya Janata Party [29]
Sikkim
(list) Pawan Kumar Chamling 12 December 1994
(7003871900000000000♠23 years, 318 days) Sikkim Democratic Front [30]
Tamil Nadu
(list) Edappadi K. Palaniswami 16 February 2017
(7002617000000000000♠1 year, 252 days) All India Anna Dravida Munnetra Kazhagam [31]
Telangana
(list) K. Chandrashekhar Rao 2 June 2014
(7003160700000000000♠4 years, 146 days) Telangana Rashtra Samithi [32]
Tripura
(list) Biplab Kumar Deb 9 March 2018
(231 Days) Bharatiya Janata Party [33]
Uttar Pradesh
(list) Yogi Adityanath 19 March 2017
(7002586000000000000♠1 year, 221 days) Bharatiya Janata Party [34]
Uttarakhand
(list) Trivendra Singh Rawat 18 March 2017
(7002587000000000000♠1 year, 222 days) Bharatiya Janata Party [35]
West Bengal
(list) Mamata Banerjee 20 May 2011
(7003271600000000000♠7 years, 159 days) All India Trinamool Congress [36]
Jump up ^ This column names only the chief minister's party. The ministry he heads may be a complex coalition of several parties and independents; those are not listed here.
^ Jump up to: a b Although Delhi and Puducherry each have an elected legislature and a council of ministers (headed by the chief minister), they are officially union territories.
Jump up ^ Similar to President's rule, Governor's rule is imposed as per Section 92 of the Constitution of Jammu and Kashmir.[15]
See also[edit]
List of current Indian governors
List of current Indian chief justices
List of current Indian legislative speakers
List of current Indian opposition leaders
List of female Indian chief ministers
List of chief ministers from the Communist Party of India (Marxist)
References[edit]
Jump up ^ Durga Das Basu. Introduction to the Constitution of India. 1960. 20th edition, 2011 reprint. pp. 241, 245. LexisNexis Butterworths Wadhwa Nagpur. ISBN 978-81-8038-559-9.
Jump up ^ "Meet Pema Khandu: India’s youngest Chief Minister". The Hindu. 17 July 2016.
Jump up ^ Chief Ministers. India.gov.in. Retrieved on 3 September 2015.
Jump up ^ "Naidu takes oath as Andhra Pradesh CM". The Hindu. 8 June 2014.
Jump up ^ "Pema Khandu sworn in as Chief Minister of Arunachal Pradesh". The Hindu. 17 July 2016.
Jump up ^ "BJP forms govt in Arunachal Pradesh". The Hindu. 31 December 2016.
Jump up ^ "Sarbananda Sonowal sworn in as first BJP CM of Assam". The Hindu. 24 May 2016.
Jump up ^ Kumar, Arun (27 July 2017). "Grand Alliance to NDA: Nitish Kumar changes partner, continues as Bihar CM". Hindustan Times. Patna. Retrieved 27 July 2017.
Jump up ^ Aarti Dhar. "Raman Singh takes oath". The Hindu. 8 December 2003.
Jump up ^ Smriti Kak Ramachandran, Shubhomoy Sikdar. "Kejriwal promises to make Delhi graft-free in 5 years". The Hindu. 14 February 2015.
Jump up ^ Nistula Hebbar, Prakash Kamat. "Parrikar takes oath in Goa as SC declines Cong. plea". The Hindu. 14 March 2017.
Jump up ^ Mahesh Langa. "Vijay Rupani sworn in; Gujarat Cabinet bears Shah’s stamp". The Hindu. 7 August 2016.
Jump up ^ Sarabjit Pandher. "Khattar sworn in". The Hindu. 26 October 2014.
Jump up ^ "Jai Ram Thakur sworn in as Himachal Chief Minister". The Indian Express. 27 December 2017.
Jump up ^ Bharti Jain. "Governor's rule imposed in Jammu & Kashmir". The Times of India. 9 January 2015.
Jump up ^ "Governor’s rule imposed in Jammu and Kashmir". The Hindu. 20 June 2018.
Jump up ^ Amarnath Tewary. "Raghuvar Das assumes office as CM". The Hindu. 28 December 2014.
Jump up ^ "Kumaraswamy sworn in; floor test tomorrow". The Hindu. 23 May 2018.
Jump up ^ C. Gouridasan Nair. "Pinarayi takes charge as Kerala Chief Minister". The Hindu. 25 May 2016.
Jump up ^ "Shivraj Chauhan sworn in Chief Minister". The Hindu. 30 November 2005.
Jump up ^ Priyanka Kakodkar. "Uddhav attends Fadnavis swearing-in". The Hindu. 31 October 2014.
Jump up ^ Isha Gupta. "BJP leader Biren Singh sworn in as Manipur Chief Minister". India Today. 15 March 2017.
Jump up ^ Shiv Sahay Singh. "Conrad Sangma sworn-in as Meghalaya CM". The Hindu. 6 March 2018.
Jump up ^ "Lal Thanhawla sworn in as Mizoram chief minister". The Times of India. 11 December 2008.
Jump up ^ Rahul Karmakar. "Neiphiu Rio takes charge as Nagaland Chief Minister again". The Hindu. 8 March 2018.
Jump up ^ N. Ramdas. "Naveen Govt. installed". The Hindu. 6 March 2000.
Jump up ^ "Puducherry: V Narayanasamy sworn in as Chief Minister". The Indian Express. 6 June 2016.
Jump up ^ "Amarinder Singh sworn in as Punjab CM". The Hindu. 17 March 2017.
Jump up ^ "Vasundhara swearing-in, a show of strength". The Hindu. 13 December 2013.
Jump up ^ "Pawan Chamling to be longest serving Chief Minister". The Hindu. 17 May 2014.
Jump up ^ T. Ramakrishnan. "Edappadi Palaniswami sworn in as Tamil Nadu Chief Minister". The Hindu. 17 February 2017.
Jump up ^ K. Srinivas Reddy. "KCR sworn in; heads cabinet of 11 ministers". The Hindu. 2 June 2014.
Jump up ^ Rahul Karmakar. "Biplab Kumar Deb sworn in as Tripura CM". The Hindu. 9 March 2018.
Jump up ^ "Yogi Adityanath takes oath as Uttar Pradesh Chief Minister". The Hindu. 19 March 2017.
Jump up ^ Kavita Upadhyay. "Trivendra Singh Rawat takes oath as Uttarakhand Chief Minister". The Hindu. 18 March 2017.
Jump up ^ "Mamata, 37 Ministers sworn in". The Hindu. 21 May 2011.
hide
v
t
e
Lists of chief ministers of Indian states (and current incumbents)
Andhra Pradesh
N. Chandrababu Naidu
Arunachal Pradesh
Pema Khandu
Assam
Sarbananda Sonowal
Bihar
Nitish Kumar
Chhattisgarh
Raman Singh
Delhi
Arvind Kejriwal
Goa
Manohar Parrikar
Gujarat
Vijay Rupani
Haryana
Manohar Lal Khattar
Himachal Pradesh
Jai Ram Thakur
Jammu and Kashmir
Vacant (Governor's rule)
Jharkhand
Raghubar Das
Karnataka
H. D. Kumaraswamy
Kerala
Pinarayi Vijayan
Madhya Pradesh
Shivraj Singh Chouhan
Maharashtra
Devendra Fadnavis
Manipur
N. Biren Singh
Meghalaya
Conrad Sangma
Mizoram
Lal Thanhawla
Nagaland
Neiphiu Rio
Odisha
Naveen Patnaik
Puducherry
V. Narayanasamy
Punjab
Amarinder Singh
Rajasthan
Vasundhara Raje
Sikkim
Pawan Kumar Chamling
Tamil Nadu
Edappadi K. Palaniswami
Telangana
K. Chandrashekhar Rao
Tripura
Biplab Kumar Deb
Uttar Pradesh
Yogi Adityanath
Uttarakhand
Trivendra Singh Rawat
West Bengal
Mamata Banerjee
Women chief ministers
From the Bharatiya Janata Party
From the Communist Party of India (Marxist)
Retrieved from "https://en.wikipedia.org/w/index.php?title=List_of_current_Indian_chief_ministers&oldid=865358452"
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values: भारत के वर्तमान मुख्यमंत्री लोग के लिस्ट – Bhojpuri

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