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# Line 27 | Line 27 | $F_ij$ be the force that particle $i$ exerts on partic
27   \end{equation}
28   A point mass interacting with other bodies moves with the
29   acceleration along the direction of the force acting on it. Let
30 < $F_ij$ be the force that particle $i$ exerts on particle $j$, and
31 < $F_ji$ be the force that particle $j$ exerts on particle $i$.
30 > $F_{ij}$ be the force that particle $i$ exerts on particle $j$, and
31 > $F_{ji}$ be the force that particle $j$ exerts on particle $i$.
32   Newton¡¯s third law states that
33   \begin{equation}
34 < F_ij = -F_ji
34 > F_{ij} = -F_{ji}
35   \label{introEquation:newtonThirdLaw}
36   \end{equation}
37  
# Line 315 | Line 315 | partition function like,
315   isolated and conserve energy, Microcanonical ensemble(NVE) has a
316   partition function like,
317   \begin{equation}
318 < \Omega (N,V,E) = e^{\beta TS}
319 < \label{introEqaution:NVEPartition}.
318 > \Omega (N,V,E) = e^{\beta TS} \label{introEquation:NVEPartition}.
319   \end{equation}
320   A canonical ensemble(NVT)is an ensemble of systems, each of which
321   can share its energy with a large heat reservoir. The distribution
# Line 394 | Line 393 | distribution,
393   \begin{equation}
394   \rho  \propto e^{ - \beta H}
395   \label{introEquation:densityAndHamiltonian}
396 + \end{equation}
397 +
398 + \subsubsection{\label{introSection:phaseSpaceConservation}Conservation of Phase Space}
399 + Lets consider a region in the phase space,
400 + \begin{equation}
401 + \delta v = \int { \ldots \int {dq_1 } ...dq_f dp_1 } ..dp_f .
402 + \end{equation}
403 + If this region is small enough, the density $\rho$ can be regarded
404 + as uniform over the whole phase space. Thus, the number of phase
405 + points inside this region is given by,
406 + \begin{equation}
407 + \delta N = \rho \delta v = \rho \int { \ldots \int {dq_1 } ...dq_f
408 + dp_1 } ..dp_f.
409   \end{equation}
410 +
411 + \begin{equation}
412 + \frac{{d(\delta N)}}{{dt}} = \frac{{d\rho }}{{dt}}\delta v + \rho
413 + \frac{d}{{dt}}(\delta v) = 0.
414 + \end{equation}
415 + With the help of stationary assumption
416 + (\ref{introEquation:stationary}), we obtain the principle of the
417 + \emph{conservation of extension in phase space},
418 + \begin{equation}
419 + \frac{d}{{dt}}(\delta v) = \frac{d}{{dt}}\int { \ldots \int {dq_1 }
420 + ...dq_f dp_1 } ..dp_f  = 0.
421 + \label{introEquation:volumePreserving}
422 + \end{equation}
423 +
424 + \subsubsection{\label{introSection:liouvilleInOtherForms}Liouville's Theorem in Other Forms}
425  
426   Liouville's theorem can be expresses in a variety of different forms
427   which are convenient within different contexts. For any two function
# Line 431 | Line 458 | expressed as
458   \label{introEquation:liouvilleTheoremInOperator}
459   \end{equation}
460  
434
461   \subsection{\label{introSection:ergodic}The Ergodic Hypothesis}
462  
463   Various thermodynamic properties can be calculated from Molecular
# Line 560 | Line 586 | H = \frac{1}{2}\left( {\frac{{\pi _1^2 }}{{I_1 }} + \f
586   }}{{I_2 }} + \frac{{\pi _3^2 }}{{I_3 }}} \right)
587   \end{equation}
588  
589 < \subsection{\label{introSection:geometricProperties}Geometric Properties}
589 > \subsection{\label{introSection:exactFlow}Exact Flow}
590 >
591   Let $x(t)$ be the exact solution of the ODE system,
592   \begin{equation}
593   \frac{{dx}}{{dt}} = f(x) \label{introEquation:ODE}
# Line 570 | Line 597 | space to itself. In most cases, it is not easy to find
597   x(t+\tau) =\varphi_\tau(x(t))
598   \]
599   where $\tau$ is a fixed time step and $\varphi$ is a map from phase
600 < space to itself. In most cases, it is not easy to find the exact
574 < flow $\varphi_\tau$. Instead, we use a approximate map, $\psi_\tau$,
575 < which is usually called integrator. The order of an integrator
576 < $\psi_\tau$ is $p$, if the Taylor series of $\psi_\tau$ agree to
577 < order $p$,
600 > space to itself. The flow has the continuous group property,
601   \begin{equation}
602 + \varphi _{\tau _1 }  \circ \varphi _{\tau _2 }  = \varphi _{\tau _1
603 + + \tau _2 } .
604 + \end{equation}
605 + In particular,
606 + \begin{equation}
607 + \varphi _\tau   \circ \varphi _{ - \tau }  = I
608 + \end{equation}
609 + Therefore, the exact flow is self-adjoint,
610 + \begin{equation}
611 + \varphi _\tau   = \varphi _{ - \tau }^{ - 1}.
612 + \end{equation}
613 + The exact flow can also be written in terms of the of an operator,
614 + \begin{equation}
615 + \varphi _\tau  (x) = e^{\tau \sum\limits_i {f_i (x)\frac{\partial
616 + }{{\partial x_i }}} } (x) \equiv \exp (\tau f)(x).
617 + \label{introEquation:exponentialOperator}
618 + \end{equation}
619 +
620 + In most cases, it is not easy to find the exact flow $\varphi_\tau$.
621 + Instead, we use a approximate map, $\psi_\tau$, which is usually
622 + called integrator. The order of an integrator $\psi_\tau$ is $p$, if
623 + the Taylor series of $\psi_\tau$ agree to order $p$,
624 + \begin{equation}
625   \psi_tau(x) = x + \tau f(x) + O(\tau^{p+1})
626   \end{equation}
627  
628 + \subsection{\label{introSection:geometricProperties}Geometric Properties}
629 +
630   The hidden geometric properties of ODE and its flow play important
631 < roles in numerical studies. Let $\varphi$ be the flow of Hamiltonian
632 < vector field, $\varphi$ is a \emph{symplectic} flow if it satisfies,
631 > roles in numerical studies. Many of them can be found in systems
632 > which occur naturally in applications.
633 >
634 > Let $\varphi$ be the flow of Hamiltonian vector field, $\varphi$ is
635 > a \emph{symplectic} flow if it satisfies,
636   \begin{equation}
637 < '\varphi^T J '\varphi = J.
637 > {\varphi '}^T J \varphi ' = J.
638   \end{equation}
639   According to Liouville's theorem, the symplectic volume is invariant
640   under a Hamiltonian flow, which is the basis for classical
# Line 591 | Line 642 | symplectomorphism. As to the Poisson system,
642   field on a symplectic manifold can be shown to be a
643   symplectomorphism. As to the Poisson system,
644   \begin{equation}
645 < '\varphi ^T J '\varphi  = J \circ \varphi
645 > {\varphi '}^T J \varphi ' = J \circ \varphi
646   \end{equation}
647 < is the property must be preserved by the integrator. It is possible
648 < to construct a \emph{volume-preserving} flow for a source free($
649 < \nabla \cdot f = 0 $) ODE, if the flow satisfies $ \det d\varphi  =
650 < 1$. Changing the variables $y = h(x)$ in a
651 < ODE\ref{introEquation:ODE} will result in a new system,
647 > is the property must be preserved by the integrator.
648 >
649 > It is possible to construct a \emph{volume-preserving} flow for a
650 > source free($ \nabla \cdot f = 0 $) ODE, if the flow satisfies $
651 > \det d\varphi  = 1$. One can show easily that a symplectic flow will
652 > be volume-preserving.
653 >
654 > Changing the variables $y = h(x)$ in a ODE\ref{introEquation:ODE}
655 > will result in a new system,
656   \[
657   \dot y = \tilde f(y) = ((dh \cdot f)h^{ - 1} )(y).
658   \]
659   The vector filed $f$ has reversing symmetry $h$ if $f = - \tilde f$.
660   In other words, the flow of this vector field is reversible if and
661 < only if $ h \circ \varphi ^{ - 1}  = \varphi  \circ h $. When
607 < designing any numerical methods, one should always try to preserve
608 < the structural properties of the original ODE and its flow.
661 > only if $ h \circ \varphi ^{ - 1}  = \varphi  \circ h $.
662  
663 + A \emph{first integral}, or conserved quantity of a general
664 + differential function is a function $ G:R^{2d}  \to R^d $ which is
665 + constant for all solutions of the ODE $\frac{{dx}}{{dt}} = f(x)$ ,
666 + \[
667 + \frac{{dG(x(t))}}{{dt}} = 0.
668 + \]
669 + Using chain rule, one may obtain,
670 + \[
671 + \sum\limits_i {\frac{{dG}}{{dx_i }}} f_i (x) = f \bullet \nabla G,
672 + \]
673 + which is the condition for conserving \emph{first integral}. For a
674 + canonical Hamiltonian system, the time evolution of an arbitrary
675 + smooth function $G$ is given by,
676 + \begin{equation}
677 + \begin{array}{c}
678 + \frac{{dG(x(t))}}{{dt}} = [\nabla _x G(x(t))]^T \dot x(t) \\
679 +  = [\nabla _x G(x(t))]^T J\nabla _x H(x(t)). \\
680 + \end{array}
681 + \label{introEquation:firstIntegral1}
682 + \end{equation}
683 + Using poisson bracket notion, Equation
684 + \ref{introEquation:firstIntegral1} can be rewritten as
685 + \[
686 + \frac{d}{{dt}}G(x(t)) = \left\{ {G,H} \right\}(x(t)).
687 + \]
688 + Therefore, the sufficient condition for $G$ to be the \emph{first
689 + integral} of a Hamiltonian system is
690 + \[
691 + \left\{ {G,H} \right\} = 0.
692 + \]
693 + As well known, the Hamiltonian (or energy) H of a Hamiltonian system
694 + is a \emph{first integral}, which is due to the fact $\{ H,H\}  =
695 + 0$.
696 +
697 +
698 + When designing any numerical methods, one should always try to
699 + preserve the structural properties of the original ODE and its flow.
700 +
701   \subsection{\label{introSection:constructionSymplectic}Construction of Symplectic Methods}
702   A lot of well established and very effective numerical methods have
703   been successful precisely because of their symplecticities even
# Line 622 | Line 713 | and difficult to use\cite{}. In dissipative systems, v
713   \end{enumerate}
714  
715   Generating function tends to lead to methods which are cumbersome
716 < and difficult to use\cite{}. In dissipative systems, variational
717 < methods can capture the decay of energy accurately\cite{}. Since
718 < their geometrically unstable nature against non-Hamiltonian
719 < perturbations, ordinary implicit Runge-Kutta methods are not
720 < suitable for Hamiltonian system. Recently, various high-order
721 < explicit Runge--Kutta methods have been developed to overcome this
722 < instability \cite{}. However, due to computational penalty involved
723 < in implementing the Runge-Kutta methods, they do not attract too
724 < much attention from Molecular Dynamics community. Instead, splitting
725 < have been widely accepted since they exploit natural decompositions
726 < of the system\cite{Tuckerman92}. The main idea behind splitting
727 < methods is to decompose the discrete $\varphi_h$ as a composition of
728 < simpler flows,
716 > and difficult to use. In dissipative systems, variational methods
717 > can capture the decay of energy accurately. Since their
718 > geometrically unstable nature against non-Hamiltonian perturbations,
719 > ordinary implicit Runge-Kutta methods are not suitable for
720 > Hamiltonian system. Recently, various high-order explicit
721 > Runge--Kutta methods have been developed to overcome this
722 > instability. However, due to computational penalty involved in
723 > implementing the Runge-Kutta methods, they do not attract too much
724 > attention from Molecular Dynamics community. Instead, splitting have
725 > been widely accepted since they exploit natural decompositions of
726 > the system\cite{Tuckerman92}.
727 >
728 > \subsubsection{\label{introSection:splittingMethod}Splitting Method}
729 >
730 > The main idea behind splitting methods is to decompose the discrete
731 > $\varphi_h$ as a composition of simpler flows,
732   \begin{equation}
733   \varphi _h  = \varphi _{h_1 }  \circ \varphi _{h_2 }  \ldots  \circ
734   \varphi _{h_n }
735   \label{introEquation:FlowDecomposition}
736   \end{equation}
737   where each of the sub-flow is chosen such that each represent a
738 < simpler integration of the system. Let $\phi$ and $\psi$ both be
739 < symplectic maps, it is easy to show that any composition of
740 < symplectic flows yields a symplectic map,
738 > simpler integration of the system.
739 >
740 > Suppose that a Hamiltonian system takes the form,
741 > \[
742 > H = H_1 + H_2.
743 > \]
744 > Here, $H_1$ and $H_2$ may represent different physical processes of
745 > the system. For instance, they may relate to kinetic and potential
746 > energy respectively, which is a natural decomposition of the
747 > problem. If $H_1$ and $H_2$ can be integrated using exact flows
748 > $\varphi_1(t)$ and $\varphi_2(t)$, respectively, a simple first
749 > order is then given by the Lie-Trotter formula
750   \begin{equation}
751 + \varphi _h  = \varphi _{1,h}  \circ \varphi _{2,h},
752 + \label{introEquation:firstOrderSplitting}
753 + \end{equation}
754 + where $\varphi _h$ is the result of applying the corresponding
755 + continuous $\varphi _i$ over a time $h$. By definition, as
756 + $\varphi_i(t)$ is the exact solution of a Hamiltonian system, it
757 + must follow that each operator $\varphi_i(t)$ is a symplectic map.
758 + It is easy to show that any composition of symplectic flows yields a
759 + symplectic map,
760 + \begin{equation}
761   (\varphi '\phi ')^T J\varphi '\phi ' = \phi '^T \varphi '^T J\varphi
762 < '\phi ' = \phi '^T J\phi ' = J.
762 > '\phi ' = \phi '^T J\phi ' = J,
763   \label{introEquation:SymplecticFlowComposition}
764   \end{equation}
765 < Suppose that a Hamiltonian system has a form with $H = T + V$
765 > where $\phi$ and $\psi$ both are symplectic maps. Thus operator
766 > splitting in this context automatically generates a symplectic map.
767 >
768 > The Lie-Trotter splitting(\ref{introEquation:firstOrderSplitting})
769 > introduces local errors proportional to $h^2$, while Strang
770 > splitting gives a second-order decomposition,
771 > \begin{equation}
772 > \varphi _h  = \varphi _{1,h/2}  \circ \varphi _{2,h}  \circ \varphi
773 > _{1,h/2} , \label{introEquation:secondOrderSplitting}
774 > \end{equation}
775 > which has a local error proportional to $h^3$. Sprang splitting's
776 > popularity in molecular simulation community attribute to its
777 > symmetric property,
778 > \begin{equation}
779 > \varphi _h^{ - 1} = \varphi _{ - h}.
780 > \label{introEquation:timeReversible}
781 > \end{equation}
782 >
783 > \subsubsection{\label{introSection:exampleSplittingMethod}Example of Splitting Method}
784 > The classical equation for a system consisting of interacting
785 > particles can be written in Hamiltonian form,
786 > \[
787 > H = T + V
788 > \]
789 > where $T$ is the kinetic energy and $V$ is the potential energy.
790 > Setting $H_1 = T, H_2 = V$ and applying Strang splitting, one
791 > obtains the following:
792 > \begin{align}
793 > q(\Delta t) &= q(0) + \dot{q}(0)\Delta t +
794 >    \frac{F[q(0)]}{m}\frac{\Delta t^2}{2}, %
795 > \label{introEquation:Lp10a} \\%
796 > %
797 > \dot{q}(\Delta t) &= \dot{q}(0) + \frac{\Delta t}{2m}
798 >    \biggl [F[q(0)] + F[q(\Delta t)] \biggr]. %
799 > \label{introEquation:Lp10b}
800 > \end{align}
801 > where $F(t)$ is the force at time $t$. This integration scheme is
802 > known as \emph{velocity verlet} which is
803 > symplectic(\ref{introEquation:SymplecticFlowComposition}),
804 > time-reversible(\ref{introEquation:timeReversible}) and
805 > volume-preserving (\ref{introEquation:volumePreserving}). These
806 > geometric properties attribute to its long-time stability and its
807 > popularity in the community. However, the most commonly used
808 > velocity verlet integration scheme is written as below,
809 > \begin{align}
810 > \dot{q}\biggl (\frac{\Delta t}{2}\biggr ) &=
811 >    \dot{q}(0) + \frac{\Delta t}{2m}\, F[q(0)], \label{introEquation:Lp9a}\\%
812 > %
813 > q(\Delta t) &= q(0) + \Delta t\, \dot{q}\biggl (\frac{\Delta t}{2}\biggr ),%
814 >    \label{introEquation:Lp9b}\\%
815 > %
816 > \dot{q}(\Delta t) &= \dot{q}\biggl (\frac{\Delta t}{2}\biggr ) +
817 >    \frac{\Delta t}{2m}\, F[q(0)]. \label{introEquation:Lp9c}
818 > \end{align}
819 > From the preceding splitting, one can see that the integration of
820 > the equations of motion would follow:
821 > \begin{enumerate}
822 > \item calculate the velocities at the half step, $\frac{\Delta t}{2}$, from the forces calculated at the initial position.
823 >
824 > \item Use the half step velocities to move positions one whole step, $\Delta t$.
825 >
826 > \item Evaluate the forces at the new positions, $\mathbf{r}(\Delta t)$, and use the new forces to complete the velocity move.
827 >
828 > \item Repeat from step 1 with the new position, velocities, and forces assuming the roles of the initial values.
829 > \end{enumerate}
830 >
831 > Simply switching the order of splitting and composing, a new
832 > integrator, the \emph{position verlet} integrator, can be generated,
833 > \begin{align}
834 > \dot q(\Delta t) &= \dot q(0) + \Delta tF(q(0))\left[ {q(0) +
835 > \frac{{\Delta t}}{{2m}}\dot q(0)} \right], %
836 > \label{introEquation:positionVerlet1} \\%
837 > %
838 > q(\Delta t) &= q(0) + \frac{{\Delta t}}{2}\left[ {\dot q(0) + \dot
839 > q(\Delta t)} \right]. %
840 > \label{introEquation:positionVerlet1}
841 > \end{align}
842 >
843 > \subsubsection{\label{introSection:errorAnalysis}Error Analysis and Higher Order Methods}
844 >
845 > Baker-Campbell-Hausdorff formula can be used to determine the local
846 > error of splitting method in terms of commutator of the
847 > operators(\ref{introEquation:exponentialOperator}) associated with
848 > the sub-flow. For operators $hX$ and $hY$ which are associate to
849 > $\varphi_1(t)$ and $\varphi_2(t$ respectively , we have
850 > \begin{equation}
851 > \exp (hX + hY) = \exp (hZ)
852 > \end{equation}
853 > where
854 > \begin{equation}
855 > hZ = hX + hY + \frac{{h^2 }}{2}[X,Y] + \frac{{h^3 }}{2}\left(
856 > {[X,[X,Y]] + [Y,[Y,X]]} \right) +  \ldots .
857 > \end{equation}
858 > Here, $[X,Y]$ is the commutators of operator $X$ and $Y$ given by
859 > \[
860 > [X,Y] = XY - YX .
861 > \]
862 > Applying Baker-Campbell-Hausdorff formula to Sprang splitting, we
863 > can obtain
864 > \begin{eqnarray*}
865 > \exp (h X/2)\exp (h Y)\exp (h X/2) & = & \exp (h X + h Y + h^2
866 > [X,Y]/4 + h^2 [Y,X]/4 \\ & & \mbox{} + h^2 [X,X]/8 + h^2 [Y,Y]/8 \\
867 > & & \mbox{} + h^3 [Y,[Y,X]]/12 - h^3 [X,[X,Y]]/24 & & \mbox{} +
868 > \ldots )
869 > \end{eqnarray*}
870 > Since \[ [X,Y] + [Y,X] = 0\] and \[ [X,X] = 0\], the dominant local
871 > error of Spring splitting is proportional to $h^3$. The same
872 > procedure can be applied to general splitting,  of the form
873 > \begin{equation}
874 > \varphi _{b_m h}^2  \circ \varphi _{a_m h}^1  \circ \varphi _{b_{m -
875 > 1} h}^2  \circ  \ldots  \circ \varphi _{a_1 h}^1 .
876 > \end{equation}
877 > Careful choice of coefficient $a_1 ,\ldot , b_m$ will lead to higher
878 > order method. Yoshida proposed an elegant way to compose higher
879 > order methods based on symmetric splitting. Given a symmetric second
880 > order base method $ \varphi _h^{(2)} $, a fourth-order symmetric
881 > method can be constructed by composing,
882 > \[
883 > \varphi _h^{(4)}  = \varphi _{\alpha h}^{(2)}  \circ \varphi _{\beta
884 > h}^{(2)}  \circ \varphi _{\alpha h}^{(2)}
885 > \]
886 > where $ \alpha  =  - \frac{{2^{1/3} }}{{2 - 2^{1/3} }}$ and $ \beta
887 > = \frac{{2^{1/3} }}{{2 - 2^{1/3} }}$. Moreover, a symmetric
888 > integrator $ \varphi _h^{(2n + 2)}$ can be composed by
889 > \begin{equation}
890 > \varphi _h^{(2n + 2)}  = \varphi _{\alpha h}^{(2n)}  \circ \varphi
891 > _{\beta h}^{(2n)}  \circ \varphi _{\alpha h}^{(2n)}
892 > \end{equation}
893 > , if the weights are chosen as
894 > \[
895 > \alpha  =  - \frac{{2^{1/(2n + 1)} }}{{2 - 2^{1/(2n + 1)} }},\beta =
896 > \frac{{2^{1/(2n + 1)} }}{{2 - 2^{1/(2n + 1)} }} .
897 > \]
898  
899   \section{\label{introSection:molecularDynamics}Molecular Dynamics}
900  
# Line 660 | Line 905 | dynamical information.
905  
906   \subsection{\label{introSec:mdInit}Initialization}
907  
908 + \subsection{\label{introSec:forceEvaluation}Force Evaluation}
909 +
910   \subsection{\label{introSection:mdIntegration} Integration of the Equations of Motion}
911  
912   \section{\label{introSection:rigidBody}Dynamics of Rigid Bodies}
913  
914 < A rigid body is a body in which the distance between any two given
915 < points of a rigid body remains constant regardless of external
916 < forces exerted on it. A rigid body therefore conserves its shape
917 < during its motion.
914 > Rigid bodies are frequently involved in the modeling of different
915 > areas, from engineering, physics, to chemistry. For example,
916 > missiles and vehicle are usually modeled by rigid bodies.  The
917 > movement of the objects in 3D gaming engine or other physics
918 > simulator is governed by the rigid body dynamics. In molecular
919 > simulation, rigid body is used to simplify the model in
920 > protein-protein docking study{\cite{Gray03}}.
921  
922 < Applications of dynamics of rigid bodies.
922 > It is very important to develop stable and efficient methods to
923 > integrate the equations of motion of orientational degrees of
924 > freedom. Euler angles are the nature choice to describe the
925 > rotational degrees of freedom. However, due to its singularity, the
926 > numerical integration of corresponding equations of motion is very
927 > inefficient and inaccurate. Although an alternative integrator using
928 > different sets of Euler angles can overcome this difficulty\cite{},
929 > the computational penalty and the lost of angular momentum
930 > conservation still remain. A singularity free representation
931 > utilizing quaternions was developed by Evans in 1977. Unfortunately,
932 > this approach suffer from the nonseparable Hamiltonian resulted from
933 > quaternion representation, which prevents the symplectic algorithm
934 > to be utilized. Another different approach is to apply holonomic
935 > constraints to the atoms belonging to the rigid body. Each atom
936 > moves independently under the normal forces deriving from potential
937 > energy and constraint forces which are used to guarantee the
938 > rigidness. However, due to their iterative nature, SHAKE and Rattle
939 > algorithm converge very slowly when the number of constraint
940 > increases.
941  
942 + The break through in geometric literature suggests that, in order to
943 + develop a long-term integration scheme, one should preserve the
944 + symplectic structure of the flow. Introducing conjugate momentum to
945 + rotation matrix $A$ and re-formulating Hamiltonian's equation, a
946 + symplectic integrator, RSHAKE, was proposed to evolve the
947 + Hamiltonian system in a constraint manifold by iteratively
948 + satisfying the orthogonality constraint $A_t A = 1$. An alternative
949 + method using quaternion representation was developed by Omelyan.
950 + However, both of these methods are iterative and inefficient. In
951 + this section, we will present a symplectic Lie-Poisson integrator
952 + for rigid body developed by Dullweber and his
953 + coworkers\cite{Dullweber1997}.
954 +
955   \subsection{\label{introSection:lieAlgebra}Lie Algebra}
956  
957 < \subsection{\label{introSection:DLMMotionEquation}The Euler Equations of Rigid Body Motion}
957 > \subsection{\label{introSection:constrainedHamiltonianRB}Constrained Hamiltonian for Rigid Body}
958  
959 < \subsection{\label{introSection:otherRBMotionEquation}Other Formulations for Rigid Body Motion}
959 > \begin{equation}
960 > H = \frac{1}{2}(p^T m^{ - 1} p) + \frac{1}{2}tr(PJ^{ - 1} P) +
961 > V(q,Q) + \frac{1}{2}tr[(QQ^T  - 1)\Lambda ].
962 > \label{introEquation:RBHamiltonian}
963 > \end{equation}
964 > Here, $q$ and $Q$  are the position and rotation matrix for the
965 > rigid-body, $p$ and $P$  are conjugate momenta to $q$  and $Q$ , and
966 > $J$, a diagonal matrix, is defined by
967 > \[
968 > I_{ii}^{ - 1}  = \frac{1}{2}\sum\limits_{i \ne j} {J_{jj}^{ - 1} }
969 > \]
970 > where $I_{ii}$ is the diagonal element of the inertia tensor. This
971 > constrained Hamiltonian equation subjects to a holonomic constraint,
972 > \begin{equation}
973 > Q^T Q = 1$, \label{introEquation:orthogonalConstraint}
974 > \end{equation}
975 > which is used to ensure rotation matrix's orthogonality.
976 > Differentiating \ref{introEquation:orthogonalConstraint} and using
977 > Equation \ref{introEquation:RBMotionMomentum}, one may obtain,
978 > \begin{equation}
979 > Q^T PJ^{ - 1}  + J^{ - 1} P^T Q = 0 . \\
980 > \label{introEquation:RBFirstOrderConstraint}
981 > \end{equation}
982  
983 < %\subsection{\label{introSection:poissonBrackets}Poisson Brackets}
983 > Using Equation (\ref{introEquation:motionHamiltonianCoordinate},
984 > \ref{introEquation:motionHamiltonianMomentum}), one can write down
985 > the equations of motion,
986 > \[
987 > \begin{array}{c}
988 > \frac{{dq}}{{dt}} = \frac{p}{m} \label{introEquation:RBMotionPosition}\\
989 > \frac{{dp}}{{dt}} =  - \nabla _q V(q,Q) \label{introEquation:RBMotionMomentum}\\
990 > \frac{{dQ}}{{dt}} = PJ^{ - 1}  \label{introEquation:RBMotionRotation}\\
991 > \frac{{dP}}{{dt}} =  - \nabla _Q V(q,Q) - 2Q\Lambda . \label{introEquation:RBMotionP}\\
992 > \end{array}
993 > \]
994 >
995 > In general, there are two ways to satisfy the holonomic constraints.
996 > We can use constraint force provided by lagrange multiplier on the
997 > normal manifold to keep the motion on constraint space. Or we can
998 > simply evolve the system in constraint manifold. The two method are
999 > proved to be equivalent. The holonomic constraint and equations of
1000 > motions define a constraint manifold for rigid body
1001 > \[
1002 > M = \left\{ {(Q,P):Q^T Q = 1,Q^T PJ^{ - 1}  + J^{ - 1} P^T Q = 0}
1003 > \right\}.
1004 > \]
1005  
1006 < \section{\label{introSection:correlationFunctions}Correlation Functions}
1006 > Unfortunately, this constraint manifold is not the cotangent bundle
1007 > $T_{\star}SO(3)$. However, it turns out that under symplectic
1008 > transformation, the cotangent space and the phase space are
1009 > diffeomorphic. Introducing
1010 > \[
1011 > \tilde Q = Q,\tilde P = \frac{1}{2}\left( {P - QP^T Q} \right),
1012 > \]
1013 > the mechanical system subject to a holonomic constraint manifold $M$
1014 > can be re-formulated as a Hamiltonian system on the cotangent space
1015 > \[
1016 > T^* SO(3) = \left\{ {(\tilde Q,\tilde P):\tilde Q^T \tilde Q =
1017 > 1,\tilde Q^T \tilde PJ^{ - 1}  + J^{ - 1} P^T \tilde Q = 0} \right\}
1018 > \]
1019 >
1020 > For a body fixed vector $X_i$ with respect to the center of mass of
1021 > the rigid body, its corresponding lab fixed vector $X_0^{lab}$  is
1022 > given as
1023 > \begin{equation}
1024 > X_i^{lab} = Q X_i + q.
1025 > \end{equation}
1026 > Therefore, potential energy $V(q,Q)$ is defined by
1027 > \[
1028 > V(q,Q) = V(Q X_0 + q).
1029 > \]
1030 > Hence,
1031 > \[
1032 > \nabla _q V(q,Q) = F(q,Q) = \sum\limits_i {F_i (q,Q)}
1033 > \]
1034 >
1035 > \[
1036 > \nabla _Q V(q,Q) = F(q,Q)X_i^t
1037 > \]
1038 >
1039 > As a common choice to describe the rotation dynamics of the rigid
1040 > body, angular momentum on body frame $\Pi  = Q^t P$ is introduced to
1041 > rewrite the equations of motion,
1042 > \begin{equation}
1043 > \begin{array}{l}
1044 > \mathop \Pi \limits^ \bullet   = J^{ - 1} \Pi ^T \Pi  + Q^T \sum\limits_i {F_i (q,Q)X_i^T }  - \Lambda  \\
1045 > \mathop Q\limits^{{\rm{   }} \bullet }  = Q\Pi {\rm{ }}J^{ - 1}  \\
1046 > \end{array}
1047 > \label{introEqaution:RBMotionPI}
1048 > \end{equation}
1049 > , as well as holonomic constraints,
1050 > \[
1051 > \begin{array}{l}
1052 > \Pi J^{ - 1}  + J^{ - 1} \Pi ^t  = 0 \\
1053 > Q^T Q = 1 \\
1054 > \end{array}
1055 > \]
1056 >
1057 > For a vector $v(v_1 ,v_2 ,v_3 ) \in R^3$ and a matrix $\hat v \in
1058 > so(3)^ \star$, the hat-map isomorphism,
1059 > \begin{equation}
1060 > v(v_1 ,v_2 ,v_3 ) \Leftrightarrow \hat v = \left(
1061 > {\begin{array}{*{20}c}
1062 >   0 & { - v_3 } & {v_2 }  \\
1063 >   {v_3 } & 0 & { - v_1 }  \\
1064 >   { - v_2 } & {v_1 } & 0  \\
1065 > \end{array}} \right),
1066 > \label{introEquation:hatmapIsomorphism}
1067 > \end{equation}
1068 > will let us associate the matrix products with traditional vector
1069 > operations
1070 > \[
1071 > \hat vu = v \times u
1072 > \]
1073  
1074 + Using \ref{introEqaution:RBMotionPI}, one can construct a skew
1075 + matrix,
1076 + \begin{equation}
1077 + (\mathop \Pi \limits^ \bullet   - \mathop \Pi \limits^ \bullet  ^T
1078 + ){\rm{ }} = {\rm{ }}(\Pi  - \Pi ^T ){\rm{ }}(J^{ - 1} \Pi  + \Pi J^{
1079 + - 1} ) + \sum\limits_i {[Q^T F_i (r,Q)X_i^T  - X_i F_i (r,Q)^T Q]} -
1080 + (\Lambda  - \Lambda ^T ) . \label{introEquation:skewMatrixPI}
1081 + \end{equation}
1082 + Since $\Lambda$ is symmetric, the last term of Equation
1083 + \ref{introEquation:skewMatrixPI}, which implies the Lagrange
1084 + multiplier $\Lambda$ is ignored in the integration.
1085 +
1086 + Hence, applying hat-map isomorphism, we obtain the equation of
1087 + motion for angular momentum on body frame
1088 + \[
1089 + \dot \pi  = \pi  \times I^{ - 1} \pi  + Q^T \sum\limits_i {F_i (r,Q)
1090 + \times X_i }
1091 + \]
1092 + In the same manner, the equation of motion for rotation matrix is
1093 + given by
1094 + \[
1095 + \dot Q = Qskew(M^{ - 1} \pi )
1096 + \]
1097 +
1098 + The free rigid body equation is an example of a non-canonical
1099 + Hamiltonian system.
1100 +
1101 + \subsection{\label{introSection:symplecticDiscretizationRB}Symplectic Integration of Euler Equations}
1102 +
1103 + \[
1104 + \varphi _{\Delta t}  = \varphi _{\Delta t/2,V}  \circ \varphi
1105 + _{\Delta t,T}  \circ \varphi _{\Delta t/2,V}
1106 + \]
1107 +
1108 + \[
1109 + \varphi _{\Delta t,T}  = \varphi _{\Delta t,R}  \circ \varphi
1110 + _{\Delta t,\pi }
1111 + \]
1112 +
1113 + \[
1114 + \varphi _{\Delta t,\pi }  = \varphi _{\Delta t/2,\pi _1 }  \circ
1115 + \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t,\pi _3 }
1116 + \circ \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t/2,\pi
1117 + _1 }
1118 + \]
1119 +
1120 + \[
1121 + \varphi _{\Delta t/2,V}  = \varphi _{\Delta t/2,F}  \circ \varphi
1122 + _{\Delta t/2,\tau }
1123 + \]
1124 +
1125 +
1126   \section{\label{introSection:langevinDynamics}Langevin Dynamics}
1127  
1128   \subsection{\label{introSection:LDIntroduction}Introduction and application of Langevin Dynamics}
# Line 729 | Line 1171 | introEquation:motionHamiltonianMomentum},
1171   \dot p &=  - \frac{{\partial H}}{{\partial x}}
1172         &= m\ddot x
1173         &= - \frac{{\partial W(x)}}{{\partial x}} - \sum\limits_{\alpha  = 1}^N {g_\alpha  \left( {x_\alpha   - \frac{{g_\alpha  }}{{m_\alpha  w_\alpha ^2 }}x} \right)}
1174 < \label{introEq:Lp5}
1174 > \label{introEquation:Lp5}
1175   \end{align}
1176   , and
1177   \begin{align}
# Line 888 | Line 1330 | Body}
1330  
1331   \subsection{\label{introSection:centersRigidBody}Centers of Rigid
1332   Body}
1333 +
1334 + \section{\label{introSection:correlationFunctions}Correlation Functions}

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