Microsoft Word - Virtualwork_maxent_revision.doc
Seeing maximum entropy from the principle of virtual work 
Qiuping A. Wang 
Institut Supérieur des Matériaux et Mécaniques Avancées du Mans,  
44 Av. Bartholdi, 72000 Le Mans, France 
Abstract 
We propose an extension of the principle of virtual work of mechanics to random 
dynamics of mechanical systems. The total virtual work of the interacting forces and inertial 
forces on every particle of the system is calculated by considering the motion of each particle. 
Then according to the principle of Lagrange-d’Alembert for dynamical equilibrium, the 
vanishing ensemble average of the virtual work gives rise to the thermodynamic equilibrium 
state with maximization of thermodynamic entropy. This approach establishes a close 
relationship between the maximum entropy approach for statistical mechanics and a 
fundamental principle of mechanics, and constitutes an attempt to give the maximum entropy 
approach, considered by many as only an inference principle based on the subjectivity of 
probability and entropy, the status of fundamental physics law. 
PACS numbers : 05.20.-y, 05.70.-a, 02.30.Xx, 
1) Introduction 
The principle of maximum entropy (maxent) is widely used in the statistical sciences and 
engineering as a powerful tool and fundamental rule. The maxent approach in statistical 
mechanics can be traced back to the works of Boltzmann and Gibbs[3] and finally be given 
the status of principle thanks to the work of Jaynes[4] who used it with Boltzmann-Gibbs-
Shannon (BGS) entropy (see below) to derive the canonical probability distribution for 
statistical mechanics in a simple manner. However, in spite of its success and popularity, 
maxent has always been at the center of scientific and philosophical discussions and has 
raised many questions and controversies[4][5][6]. A central question is why a thermodynamic 
system chooses the equilibrium microstates such that the BGS entropy gets to maximum. As a 
basic assumption of scientific theory, maxent is not directly or indirectly related to 
observation and undoubted facts. In the literature, maxent is postulated as such or justified 
either a priori by the second laws with additional hypothesis such as the entropy functional 
(Boltzmann or Shannon entropy)[6], or a posteriori by the correctness of the probability 
distributions derived from it[4]. In statistical inference theory, it was often justified by 
intuitive arguments based on the subjectivity of probability[4] or by relating it to other 
principles such as the consistency requirement and the principle of insufficient reason of 
Laplace, which have been the object of considerable criticisms[5].  
Another important question about maxent is whether or not the BGS entropy is unique as 
the measure of uncertainty or disorder that can be maximized in order to determine 
probability distributions. This was already an question raised 40 years ago by the scientists 
who tried to generalize the Shannon entropy by mathematical considerations [9][10].  
In the present work, we try to contribute to the debate around maxent by an attempt to 
derive maxent from a well known fundamental principle of classical mechanics, the virtual 
work principle or Lagrange-d’Alembert principle (LAP) [1][2] without additional hypotheses 
to LAP and about entropy property. LAP is widely used in physical sciences as well as in 
mechanical engineering. It is a basic principle capable of yielding all the basic laws of statics 
and of dynamics of mechanical systems. It is in addition a simple, clearly defined, easily 
understandable and palpable law of physics. It is hoped that this derivation is scientifically 
and pedagogically beneficial for the understanding of maxent and of the relevant questions 
and controversies around it. In this work, the term entropy, denoted by S, is used in the sense 
of the second law of thermodynamics for equilibrium system. 
2) Principle of virtual work 
The variational calculus in mechanics has a long history which may be traced back to 
Galilei and other physicists of his time who studied the equilibrium problem of statics with 
LAP (or virtual displacement1). LAP gets unified and concise mathematical forms thanks to 
Lagrange[1] and d’Alembert[2] and is considered as a most basic principle of mechanics from 
which all the fundamental laws of statics and dynamics can be understood thoroughly.   
LAP says that the total work done by all forces acting on a system in static equilibrium is 
zero on all possible virtual displacements which are consistent with the constraints of the 
system. Let us suppose a simple case of a system of N points of mass in equilibrium under the 
action of N forces Fi (i=1,2,…N) with Fi on the point i, and imagine virtual displacement of 
each point ir
δ  for the point i. According to LAP, the virtual work Wδ  of all the forces Fi on 
all ir
δ  cancels itself for static equilibrium, i.e.  
i rFW
     (1) 
This principle for statics was extended to dynamical equilibrium by d’Alembert[2] in the LAP 
by adding the initial force iiam
− on each point: 
=⋅−∑=
i ramFW
     (2) 
where mi is the mass of the poin i and ia
 its acceleration. From this principle, we can not only 
derive Newtonian equation of dynamics, but also other fundamental principles such as least 
action principle.  
3) Why maximum thermodynamic entropy ? 
We suppose that the mechanics laws are usable not only for mechanical system containing 
small number of particles in regular motion, but also for large number of particles in random 
and stochastic motion for which one has to use statistical approach introducing probability 
distribution of mechanical states. Let us first consider an ensemble of equilibrium systems, 
                                                 
1 In mechanics, the virtual displacement of a system is a kind of imaginary infinitesimal displacement with no 
time passage and no influence on the forces. It should be perpendicular to the constraint forces. 
each composed of N particles in random motion with vi
v  the velocity of the particle i. It will 
be shown that the result for canonical ensemble can be easily extended to microcanonical 
ensemble and grand-canonical ensemble. Without loss of generality, let us look at a system 
without macroscopic motion, i.e., 0
v .  
We imagine that the system in thermodynamic equilibrium leaves the equilibrium state by 
a reversible infinitesimal virtual process.  Let Fi
 be the force on a particle i of the system at 
that moment. Fi
 includes all the interacting forces particles-particles and particles-walls of 
the container. During the virtual process, each particle with acceleration ir&&
 has a virtual 
displacement r i
vδ . The total virtual work on this displacement is given by 
i rrmFW
δδ ⋅−∑=
     (3) 
Although the sum of the accelerations of all the particles vanishes, i.e., 0
irm &&
v , the 
acceleration ir&&
 on each particle can be nonzero. So in general 0
i rrm
v&&v δ . As a matter of 
fact, we have kiiiiii ermrrmrrm δδδδ ==⋅=⋅ )2
v&v&vv&&v  where eki is the kinetic energy of the 
particle. On the other hand, we suppose these are no dissipative forces in the system or on the 
particles. It means that the energy of the system will not change if the system is completely 
closed and isolated. Let epi be the potential energy of a particle i subjet to the force Fi
, we 
should have pii eF −∇=
 and 
i ererF ∑−=⋅∑ ∇−=⋅∑
=== 111
vvv  
     (4) 
So finally it follows that 
∑−=∑ +−=
kipi eeeW
)( δδδδ  
     (5) 
where eiδ  is a virtual variation of the total energy eee kipii +=  of the particle i and ∑=
the total energy of the N particles. 
At this stage, no statistics has been done. The particles are treated as if they had regular 
dynamics. As a matter of fact, when the dynamics is random such as in a thermodynamic 
system, a microscopic process can leads the N particles from a given microstate to different 
microstates j with different probability pj (j=1,2 … w). If we looks at the system in the phase 
space, the considered process with given virtual displacements can take different directions or 
paths each leading to a given microstate with some likelihood. Hence the virtual work given 
by Eq.(5) is not a correct and complete expression for the random dynamics. It is in fact the 
virtual work of a possible process leading to a microstate j. It should be written as 
( )∑−=
jij eW
δδ  instead of Eq.(5). This "partial" virtual work cannot be used in the LAP 
since it is only a possible part of the total virtual work whose correct expression needs the 
introduction of the probability distribution of microstates pj. Logically, the total virtual work 
should be an average of the work given by Eq.(5) over all the possible microstates, i.e., 
WpW j
jδδ ∑=
     (6) 
This expression is essential in the application of LAP, an approach originally for regular 
dynamics, to irregular and random dynamics. Eq.(6) makes it possible to introduce the 
dynamic uncertainty (entropy) into the variational approach as shown below. In terms of 
thermodynamic ensemble, Eq.(6) is the ensemble average of the virtual works of all the 
members of an ensemble of systems distributed over the microstates. It is this average which 
is measurable and has a physical sense in the case of random dynamics just as the usual 
average energy in thermodynamics. It is not conceivable to let the partial virtual work of 
Eq.(5) vanish because this would signifies that the random motion in each direction in phase 
space of the virtual process is regular according to LAP and there would be only one direction 
or phase path of the virtual process leading to only one microstate, which is contradictory 
with the hypothesis of the random dynamics. This reasoning is the essential difference of the 
present approach from the simple search for mechanics principle and a direct use of the latter 
to each possible state or trajectory in phase space. The use of mechanics principle in regular 
way in general yields regular mechanical laws irrelevant to thermodynamics. The dynamical 
randomness is the fact that not all the possible states or trajectories follow the regular 
mechanical laws due to noises, certain chaos, or to quantum mechanics in which the 
Newtonian laws are obeyed only statistically. The statistical Newtonian second law given in 
[11] is an example.  
Eq.(6) can be accounted for in an explicit way as follows. A microstate j is some 
distribution of the N particles over the one particle states k with energy εk where k varies from, 
say, 1 to g (g can be very large). We imagine Nj identical particles distributed over the g states 
at a microstate j which is here a combination of g numbers nk of particles over the g states, 
i.e., j={n1, n2, … ng, }. We naturally have ( )∑=
jkj nN
 and ( ) ε k
jkj nE ∑=
. During the 
process of virtual work, only the energy of the particle can change (the fact that virtual work 
does not affect nk can be understood from quantum point of view since kε  is discrete but 
virtual work is infinitesimal and continuous). For a given j with probability pj, the virtual 
work given in Eq.(5) is now 
∑+−=∑ ∑+−∑ =−=
kjkkjk
kjkj nEnnnW εδδεδεδδεδ )()()()( .      (7) 
The first term of the right hand side is the total energy variation due to the one particle energy 
variation kδε  caused by the virtual work as well as to the variation in particle number jNδ  of 
the system. The second term is just the energy variation caused by the particle number 
variation ∑=
jkj nN )(δδ . Hence Eq.(6) reads 
NEnEnpEpW
j δμδδεδδεδδ +−=∑+−=∑∑+∑−= )( . 
     (8) 
where we put an expression for the chemical potential Nn
kk δδεμ /∑=  with 
( ) ∑=∑ ∑=∑=
j nnpNpN δδδδ  and j
j EpE δδ ∑= . Since  ∑−=
jj pEEE δδδ  and  
pNNN δδδ  with j
jEpE ∑=  and j
jNpN ∑= , we get 
∑ −++−∑ =−∑ ++−=
jj pNENEpNNpEEW δμμδδδμμδδδδ )(  
     (9) 
Now using the first law NWQE μδδδδ +−=  for Grand-canonical ensemble, we identify the 
heat transfer ∑ −=
jjj pNEQ δμδ )( . For a reversible virtual process, we can write 
∑ −==
jjj pNEQS δμββδδ )(  and get  
NEW ++−= . 
     (10) 
where S is the thermodynamic entropy of the second law.  
The following variational calculus for different ensemble is straightforward. According to 
LAP 0=Wδ , we have 
0)( =+− NES βμβδ       (11) 
which is the usual algorithm of maxent for grand-canonical ensemble. The only difference is 
that here the "constraints" associated with energy and particle number appear in the 
variational calculus as a simple consequence of LAP, in contrast to the introduction of these 
constraints in the inference theory or inferential statistical mechanics[4] by the argument that 
an averaged value of an observable quantity represents a factual information to be put into the 
maximization of information in order to derive unbiased probability distribution[5]. 
In order to see further details about this maxent, let us suppose the entropy is a function of 
the probability distribution pj of the considered moment, i.e., ...)...( ,2,1 jpppfS = . We can 
write j
S δδ ∑
=  due to the variations of the virtual process. On the other hand, we have 
jjj pNES δμβδ )( which implies 
0)( =+−∑
δβμβ . 
     (12) 
By virtue of the normalization condition 0=∑
jpδ , one can prove [12] that 
αβμβ =+−
     (13) 
with a constant α. Eq.(13) can be used for deriving the probability distribution of the 
nonequilibrium component of the dynamics if the functional f is given. Inversely, if the 
probability distribution is known, one can derive the functional of S. 
For canonical ensemble, we have 0=Nδ  and 
0)( =− ES βδ       (14) 
or, by the same argument as above, 
αβ =−
     (15) 
For microcanonical ensemble, the system is completely closed and isolated with constant 
energy 0=Eδ  and constant particle number 0=Nδ . When the virtual displacements occur, 
the total virtual work would be transformed into virtual heat such that Eq.(10) becomes 
0=− QW δδ . LAP 0=Wδ  leads to 
0=Sδ  or α=
     (16) 
which necessarily yields uniform probability distribution over the different microstates j, i.e., 
pj =1/w whatever is the form of the entropy S. Note that here the uniform distribution over the 
microstates is not an a priori assumption, but a consequence of LAP.  
This equiprobability can be proven as follows only by supposing that ...)...( ,2,1 jpppfS =  
is a strictly increasing or decreasing function of all pj throughout the interval 10 ≤≤ jp , i.e., 
its derivatives 0or  0 <>
 and are zero only at some finite number of points on the 
interval. However, Eq.(16) tells us that α=
 is a constant independent of pj, implying that 
...)...( ,2,1 jpppfS =  is either a linear function of all pj, or all pj are identical. It is evident that 
entropy cannot be linear function of pj. The equal probability of all microstates follows.  
The conclusion of this section is that, at thermodynamic equilibrium, the maxent under the 
constraints of energy is a consequence of the equilibrium condition LAP extended to random 
motion. From Eq.(8), one notices that maxent can be written in the following concise form for 
any ensemble with n random variables Xi (i=1,2 …n): 
ii Xδχ  
where iχ  is some constant corresponding to iχ . For grand-canonical ensemble, 
     (17) 
this is 0=− NE δμδ  and for canonical ensemble, it is 0=Eδ . 
We stress that in the above derivation, the only essential assumptions or fundamental 
physical hypotheses used before the LAP are the first and second laws of thermodynamics for 
equilibrium system and reversible process. Hence the three algorithms of maxent for the three 
statistical ensembles are in principle valid for all systems for which the first and second laws 
are valid. We would like to mention here that this derivation of maxent is not associated with 
any given form of entropy like in the original version of Jaynes principle.  
4) Concluding remarks 
This work shows that the maximum entropy principle has a close connection with the 
fundamental principle of classical mechanics, the principle of virtual work, i.e., for a 
mechanical system to be in thermodynamics equilibrium with maximum entropy, the total 
virtual work of all the forces on all the elements (particles) of the system should vanish. 
Indeed, if one admits that thermodynamic entropy is a measure of dynamical disorder and 
randomness, it is natural to say that this disorder must get to maximum in order that all the 
random forces act on each degree of freedom of the motion in such a way that over any 
possible (virtual) displacement, the work of all the forces is zero. In other words, this 
vanishing work can be obtained if and only if the randomness of the forces is at maximum 
over all degree of freedom allowed by the constraints to get stable equilibrium state.  
To our opinion, the present result is helpful not only for the understanding of maxent 
derived from a more basic and well understood mechanical principle, it also shows that 
entropy in physics is not necessarily a subjective quantity reaching maximum for correct 
inference, and that maximum entropy is a law of physics but not merely an inference 
principle.  
After finishing this paper, the author became aware of a work of Plastino and Curado[12] 
on the equivalence between the particular thermodynamic relation ES βδδ =  and maxent in 
the derivation of probability distribution. They consider the particular thermodynamic process 
affecting only the microstate population in order to find a different way from maxent to derive 
probability. The work part is not considered in their work. Their analysis is pertinent and 
consequential. The present work provides a substantial support of their reasoning from a basic 
principle of mechanics.   
References 
[1] J.L. Lagrange,   Mécanique analytique, Blanchard, reprint , Paris  (1965)  (Also: 
Oeuvres, Vol. 11.) 
[2] J. D’Alembert,   Traité de dynamique, Editions Jacques Cabay , Sceaux  (1990) 
[3] J. Willard Gibbs, Principes élémentaires de mécanique statistique (Paris, Hermann, 
1998) 
[4] E.T. Jaynes, The evolution of Carnot's principle, The opening talk at the EMBO 
Workshop on Maximum Entropy Methods in x-ray crystallographic and biological 
macromolecule structure determination, Orsay, France, April 24-28, 1984; Gibbs 
vs Boltzmann entropies, American Journal of Physics, 33,391(1965) ; Where do 
we go from here? in Maximum entropy and Bayesian methods in inverse problems, 
pp.21-58, eddited by C. Ray Smith and W.T. Grandy Jr., D. Reidel, Publishing 
Company (1985) 
[5] Jos Uffink, Can the maximum entropy principle be explained as a consistency 
requirement, Studies in History and Philosophy of Modern Physics, 26B (1995): 
223-261 
[6] L.M. Martyushev and V.D. Seleznev, Maximum entropy production principle in 
physics, chemistry and biology, Physics Reports, 426, 1-45 (2006) 
[7] Y.P. Terletskii, Statistical physics, North-Holland Publishing Company, 
Amsterdam, 1971 
[8] Q.A. Wang, Some invariant probability and entropy as a maximizable measure of 
uncertainty, to appear in J. Phys. A (2008); cond-mat/0612076 
[9] A. Rényi, Calcul de probabilité, Paris, Dunod, 1966, P522 
A. Wehrl, Rev. Mod. Phys., 50(1978)221 
[10] M.D. Esteban, Kybernetika, 31(1995)337 
[11] R.P. Feynman and A.R. Hibbs, Quantum mechanics and path integrals, 
McGraw-Hill Publishing Company, New York, 1965 
[12] A. Plastino and E.F.M. Curado, Phys. Rev. E, 72(2005)047103; 
E.F.M. Curado and A. Plastino, arXiv:cond-mat/0601076; also cond-mat/0509070

  We propose an extension of the principle of virtual work of mechanics to
random dynamics of mechanical systems. The total virtual work of the
interacting forces and inertial forces on every particle of the system is
calculated by considering the motion of each particle. Then according to the
principle of Lagrange-d'Alembert for dynamical equilibrium, the vanishing
ensemble average of the virtual work gives rise to the thermodynamic
equilibrium state with maximization of thermodynamic entropy. This approach
establishes a close relationship between the maximum entropy approach for
statistical mechanics and a fundamental principle of mechanics, and constitutes
an attempt to give the maximum entropy approach, considered by many as only an
inference principle based on the subjectivity of probability and entropy, the
status of fundamental physics law.

Introduction 
The principle of maximum entropy (maxent) is widely used in the statistical sciences and 
engineering as a powerful tool and fundamental rule. The maxent approach in statistical 
mechanics can be traced back to the works of Boltzmann and Gibbs[3] and finally be given 
the status of principle thanks to the work of Jaynes[4] who used it with Boltzmann-Gibbs-
Shannon (BGS) entropy (see below) to derive the canonical probability distribution for 
statistical mechanics in a simple manner. However, in spite of its success and popularity, 
maxent has always been at the center of scientific and philosophical discussions and has 
raised many questions and controversies[4][5][6]. A central question is why a thermodynamic 
system chooses the equilibrium microstates such that the BGS entropy gets to maximum. As a 
basic assumption of scientific theory, maxent is not directly or indirectly related to 
observation and undoubted facts. In the literature, maxent is postulated as such or justified 
either a priori by the second laws with additional hypothesis such as the entropy functional 
(Boltzmann or Shannon entropy)[6], or a posteriori by the correctness of the probability 
distributions derived from it[4]. In statistical inference theory, it was often justified by 
intuitive arguments based on the subjectivity of probability[4] or by relating it to other 
principles such as the consistency requirement and the principle of insufficient reason of 
Laplace, which have been the object of considerable criticisms[5].  
Another important question about maxent is whether or not the BGS entropy is unique as 
the measure of uncertainty or disorder that can be maximized in order to determine 
probability distributions. This was already an question raised 40 years ago by the scientists 
who tried to generalize the Shannon entropy by mathematical considerations [9][10].  
In the present work, we try to contribute to the debate around maxent by an attempt to 
derive maxent from a well known fundamental principle of classical mechanics, the virtual 
work principle or Lagrange-d’Alembert principle (LAP) [1][2] without additional hypotheses 
to LAP and about entropy property. LAP is widely used in physical sciences as well as in 
mechanical engineering. It is a basic principle capable of yielding all the basic laws of statics 
and of dynamics of mechanical systems. It is in addition a simple, clearly defined, easily 
understandable and palpable law of physics. It is hoped that this derivation is scientifically 
and pedagogically beneficial for the understanding of maxent and of the relevant questions 
and controversies around it. In this work, the term entropy, denoted by S, is used in the sense 
of the second law of thermodynamics for equilibrium system. 
2) Principle of virtual work 
The variational calculus in mechanics has a long history which may be traced back to 
Galilei and other physicists of his time who studied the equilibrium problem of statics with 
LAP (or virtual displacement1). LAP gets unified and concise mathematical forms thanks to 
Lagrange[1] and d’Alembert[2] and is considered as a most basic principle of mechanics from 
which all the fundamental laws of statics and dynamics can be understood thoroughly.   
LAP says that the total work done by all forces acting on a system in static equilibrium is 
zero on all possible virtual displacements which are consistent with the constraints of the 
system. Let us suppose a simple case of a system of N points of mass in equilibrium under the 
action of N forces Fi (i=1,2,…N) with Fi on the point i, and imagine virtual displacement of 
each point ir
δ  for the point i. According to LAP, the virtual work Wδ  of all the forces Fi on 
all ir
δ  cancels itself for static equilibrium, i.e.  
i rFW
     (1) 
This principle for statics was extended to dynamical equilibrium by d’Alembert[2] in the LAP 
by adding the initial force iiam
− on each point: 
=⋅−∑=
i ramFW
     (2) 
where mi is the mass of the poin i and ia
 its acceleration. From this principle, we can not only 
derive Newtonian equation of dynamics, but also other fundamental principles such as least 
action principle.  
3) Why maximum thermodynamic entropy ? 
We suppose that the mechanics laws are usable not only for mechanical system containing 
small number of particles in regular motion, but also for large number of particles in random 
and stochastic motion for which one has to use statistical approach introducing probability 
distribution of mechanical states. Let us first consider an ensemble of equilibrium systems, 
                                                 
1 In mechanics, the virtual displacement of a system is a kind of imaginary infinitesimal displacement with no 
time passage and no influence on the forces. It should be perpendicular to the constraint forces. 
each composed of N particles in random motion with vi
v  the velocity of the particle i. It will 
be shown that the result for canonical ensemble can be easily extended to microcanonical 
ensemble and grand-canonical ensemble. Without loss of generality, let us look at a system 
without macroscopic motion, i.e., 0
v .  
We imagine that the system in thermodynamic equilibrium leaves the equilibrium state by 
a reversible infinitesimal virtual process.  Let Fi
 be the force on a particle i of the system at 
that moment. Fi
 includes all the interacting forces particles-particles and particles-walls of 
the container. During the virtual process, each particle with acceleration ir&&
 has a virtual 
displacement r i
vδ . The total virtual work on this displacement is given by 
i rrmFW
δδ ⋅−∑=
     (3) 
Although the sum of the accelerations of all the particles vanishes, i.e., 0
irm &&
v , the 
acceleration ir&&
 on each particle can be nonzero. So in general 0
i rrm
v&&v δ . As a matter of 
fact, we have kiiiiii ermrrmrrm δδδδ ==⋅=⋅ )2
v&v&vv&&v  where eki is the kinetic energy of the 
particle. On the other hand, we suppose these are no dissipative forces in the system or on the 
particles. It means that the energy of the system will not change if the system is completely 
closed and isolated. Let epi be the potential energy of a particle i subjet to the force Fi
, we 
should have pii eF −∇=
 and 
i ererF ∑−=⋅∑ ∇−=⋅∑
=== 111
vvv  
     (4) 
So finally it follows that 
∑−=∑ +−=
kipi eeeW
)( δδδδ  
     (5) 
where eiδ  is a virtual variation of the total energy eee kipii +=  of the particle i and ∑=
the total energy of the N particles. 
At this stage, no statistics has been done. The particles are treated as if they had regular 
dynamics. As a matter of fact, when the dynamics is random such as in a thermodynamic 
system, a microscopic process can leads the N particles from a given microstate to different 
microstates j with different probability pj (j=1,2 … w). If we looks at the system in the phase 
space, the considered process with given virtual displacements can take different directions or 
paths each leading to a given microstate with some likelihood. Hence the virtual work given 
by Eq.(5) is not a correct and complete expression for the random dynamics. It is in fact the 
virtual work of a possible process leading to a microstate j. It should be written as 
( )∑−=
jij eW
δδ  instead of Eq.(5). This "partial" virtual work cannot be used in the LAP 
since it is only a possible part of the total virtual work whose correct expression needs the 
introduction of the probability distribution of microstates pj. Logically, the total virtual work 
should be an average of the work given by Eq.(5) over all the possible microstates, i.e., 
WpW j
jδδ ∑=
     (6) 
This expression is essential in the application of LAP, an approach originally for regular 
dynamics, to irregular and random dynamics. Eq.(6) makes it possible to introduce the 
dynamic uncertainty (entropy) into the variational approach as shown below. In terms of 
thermodynamic ensemble, Eq.(6) is the ensemble average of the virtual works of all the 
members of an ensemble of systems distributed over the microstates. It is this average which 
is measurable and has a physical sense in the case of random dynamics just as the usual 
average energy in thermodynamics. It is not conceivable to let the partial virtual work of 
Eq.(5) vanish because this would signifies that the random motion in each direction in phase 
space of the virtual process is regular according to LAP and there would be only one direction 
or phase path of the virtual process leading to only one microstate, which is contradictory 
with the hypothesis of the random dynamics. This reasoning is the essential difference of the 
present approach from the simple search for mechanics principle and a direct use of the latter 
to each possible state or trajectory in phase space. The use of mechanics principle in regular 
way in general yields regular mechanical laws irrelevant to thermodynamics. The dynamical 
randomness is the fact that not all the possible states or trajectories follow the regular 
mechanical laws due to noises, certain chaos, or to quantum mechanics in which the 
Newtonian laws are obeyed only statistically. The statistical Newtonian second law given in 
[11] is an example.  
Eq.(6) can be accounted for in an explicit way as follows. A microstate j is some 
distribution of the N particles over the one particle states k with energy εk where k varies from, 
say, 1 to g (g can be very large). We imagine Nj identical particles distributed over the g states 
at a microstate j which is here a combination of g numbers nk of particles over the g states, 
i.e., j={n1, n2, … ng, }. We naturally have ( )∑=
jkj nN
 and ( ) ε k
jkj nE ∑=
. During the 
process of virtual work, only the energy of the particle can change (the fact that virtual work 
does not affect nk can be understood from quantum point of view since kε  is discrete but 
virtual work is infinitesimal and continuous). For a given j with probability pj, the virtual 
work given in Eq.(5) is now 
∑+−=∑ ∑+−∑ =−=
kjkkjk
kjkj nEnnnW εδδεδεδδεδ )()()()( .      (7) 
The first term of the right hand side is the total energy variation due to the one particle energy 
variation kδε  caused by the virtual work as well as to the variation in particle number jNδ  of 
the system. The second term is just the energy variation caused by the particle number 
variation ∑=
jkj nN )(δδ . Hence Eq.(6) reads 
NEnEnpEpW
j δμδδεδδεδδ +−=∑+−=∑∑+∑−= )( . 
     (8) 
where we put an expression for the chemical potential Nn
kk δδεμ /∑=  with 
( ) ∑=∑ ∑=∑=
j nnpNpN δδδδ  and j
j EpE δδ ∑= . Since  ∑−=
jj pEEE δδδ  and  
pNNN δδδ  with j
jEpE ∑=  and j
jNpN ∑= , we get 
∑ −++−∑ =−∑ ++−=
jj pNENEpNNpEEW δμμδδδμμδδδδ )(  
     (9) 
Now using the first law NWQE μδδδδ +−=  for Grand-canonical ensemble, we identify the 
heat transfer ∑ −=
jjj pNEQ δμδ )( . For a reversible virtual process, we can write 
∑ −==
jjj pNEQS δμββδδ )(  and get  
NEW ++−= . 
     (10) 
where S is the thermodynamic entropy of the second law.  
The following variational calculus for different ensemble is straightforward. According to 
LAP 0=Wδ , we have 
0)( =+− NES βμβδ       (11) 
which is the usual algorithm of maxent for grand-canonical ensemble. The only difference is 
that here the "constraints" associated with energy and particle number appear in the 
variational calculus as a simple consequence of LAP, in contrast to the introduction of these 
constraints in the inference theory or inferential statistical mechanics[4] by the argument that 
an averaged value of an observable quantity represents a factual information to be put into the 
maximization of information in order to derive unbiased probability distribution[5]. 
In order to see further details about this maxent, let us suppose the entropy is a function of 
the probability distribution pj of the considered moment, i.e., ...)...( ,2,1 jpppfS = . We can 
write j
S δδ ∑
=  due to the variations of the virtual process. On the other hand, we have 
jjj pNES δμβδ )( which implies 
0)( =+−∑
δβμβ . 
     (12) 
By virtue of the normalization condition 0=∑
jpδ , one can prove [12] that 
αβμβ =+−
     (13) 
with a constant α. Eq.(13) can be used for deriving the probability distribution of the 
nonequilibrium component of the dynamics if the functional f is given. Inversely, if the 
probability distribution is known, one can derive the functional of S. 
For canonical ensemble, we have 0=Nδ  and 
0)( =− ES βδ       (14) 
or, by the same argument as above, 
αβ =−
     (15) 
For microcanonical ensemble, the system is completely closed and isolated with constant 
energy 0=Eδ  and constant particle number 0=Nδ . When the virtual displacements occur, 
the total virtual work would be transformed into virtual heat such that Eq.(10) becomes 
0=− QW δδ . LAP 0=Wδ  leads to 
0=Sδ  or α=
     (16) 
which necessarily yields uniform probability distribution over the different microstates j, i.e., 
pj =1/w whatever is the form of the entropy S. Note that here the uniform distribution over the 
microstates is not an a priori assumption, but a consequence of LAP.  
This equiprobability can be proven as follows only by supposing that ...)...( ,2,1 jpppfS =  
is a strictly increasing or decreasing function of all pj throughout the interval 10 ≤≤ jp , i.e., 
its derivatives 0or  0 <>
 and are zero only at some finite number of points on the 
interval. However, Eq.(16) tells us that α=
 is a constant independent of pj, implying that 
...)...( ,2,1 jpppfS =  is either a linear function of all pj, or all pj are identical. It is evident that 
entropy cannot be linear function of pj. The equal probability of all microstates follows.  
The conclusion of this section is that, at thermodynamic equilibrium, the maxent under the 
constraints of energy is a consequence of the equilibrium condition LAP extended to random 
motion. From Eq.(8), one notices that maxent can be written in the following concise form for 
any ensemble with n random variables Xi (i=1,2 …n): 
ii Xδχ  
where iχ  is some constant corresponding to iχ . For grand-canonical ensemble, 
     (17) 
this is 0=− NE δμδ  and for canonical ensemble, it is 0=Eδ . 
We stress that in the above derivation, the only essential assumptions or fundamental 
physical hypotheses used before the LAP are the first and second laws of thermodynamics for 
equilibrium system and reversible process. Hence the three algorithms of maxent for the three 
statistical ensembles are in principle valid for all systems for which the first and second laws 
are valid. We would like to mention here that this derivation of maxent is not associated with 
any given form of entropy like in the original version of Jaynes principle.  
4) Concluding remarks 
This work shows that the maximum entropy principle has a close connection with the 
fundamental principle of classical mechanics, the principle of virtual work, i.e., for a 
mechanical system to be in thermodynamics equilibrium with maximum entropy, the total 
virtual work of all the forces on all the elements (particles) of the system should vanish. 
Indeed, if one admits that thermodynamic entropy is a measure of dynamical disorder and 
randomness, it is natural to say that this disorder must get to maximum in order that all the 
random forces act on each degree of freedom of the motion in such a way that over any 
possible (virtual) displacement, the work of all the forces is zero. In other words, this 
vanishing work can be obtained if and only if the randomness of the forces is at maximum 
over all degree of freedom allowed by the constraints to get stable equilibrium state.  
To our opinion, the present result is helpful not only for the understanding of maxent 
derived from a more basic and well understood mechanical principle, it also shows that 
entropy in physics is not necessarily a subjective quantity reaching maximum for correct 
inference, and that maximum entropy is a law of physics but not merely an inference 
principle.  
After finishing this paper, the author became aware of a work of Plastino and Curado[12] 
on the equivalence between the particular thermodynamic relation ES βδδ =  and maxent in 
the derivation of probability distribution. They consider the particular thermodynamic process 
affecting only the microstate population in order to find a different way from maxent to derive 
probability. The work part is not considered in their work. Their analysis is pertinent and 
consequential. The present work provides a substantial support of their reasoning from a basic 
principle of mechanics.   
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