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22

Chaotic is not the same as random. A chaotic system is entirely deterministic, while a random system is entirely non-deterministic. Chaotic means that infinitesimally close initial conditions lead to arbitrarily large divergences as the system evolves. But it's impossible, practically speaking, to reproduce the same initial conditions twice. Given ...


18

It has been shown by Eichhorn, Linz and Hänggi in 2000 that the numerical values of Lyapunov exponents are invariant under any invertible variable transform. This is just a reformulation of the fact that they are metric invariant, because the authors presume the norm $|\cdot|$ to be an arbitrary norm in the given coordinates - just it's basic properties such ...


11

The concept of network is very general and can be applied to many physical, biological, neuronal, technological and social phenomenon. Any system with distinguishable individual parts interacting with each other can be described by a type of network. With its applicability to many real world problem, a whole individual research field called network science ...


6

I) In this answer we discuss a systematic approach to linearization and stability analysis. Imagine that the physical system under consideration is described by an autonomous Lagrangian $L=L(q,\dot{q})$ of $n$ generalized coordinates $$\tag{1} q~=~(q^1, \ldots, q^n)~\in~ \mathbb{R}^n.$$ One of the first questions one would like to ask is, if a specific ...


6

Perhaps a better question to ask is: why is a single pendulum non-chaotic? Almost all real systems are chaotic at least to some extent; the fact that we can write out the solution for a single pendulum for all points in time is really quite peculiar, and only true because it is a highly simplified system. The reason these non-chaotic systems are so prevalent ...


5

Yes. The same system can - at least in many cases - be described by either a stochastic process with memory or by a Markov process. The point is that in order to write it as a Markov process, one must add enough variables encoding the memory. For example, an autoregressive moving average (ARMA) process is defined as a process with memory, but each such ...


4

It doesn't look that much like a normal distribution to me - particularly on the x axis, the right-hand tail looks heavier than the left, whereas the left one is much longer. But, generally speaking, normal distributions tend to arise when lots of small, independently distributed random numbers (of any distribution) are added together. (The theorem that ...


4

An harmonic oscillator. When evolving with time, its joint distribution in (p,x) is given by the Boltzman distribution: $e^{-H(p,x)}$, but the energy along a trajectory is constant. Nevertheless if write explicitly the hamiltonian you will find that $e^{-H} = e^{-p^2/2 - x^2/2}$ and although the energy is constant the individual distributions of $x$ and $...


4

At some point, whatever quantities you're talking about in "experimental sciences" refer directly or indirectly to something measurable with a precision limited to a certain number of siginificant digits. Anything smaller than the quantities in the frame of reference of the discussion by more orders of magnitude than this number of significant digits may be ...


4

The dimension should be 3 or larger. If the dimension is smaller then 3 the existence and uniqueness theorem for differential equations will tell you that functions can't intersect (since you want them to be continuous and differentiable). In 1 dimension this means you can only have movement in one direction In 2 dimensions this means that your value ...


4

Chaos isn't easy to define precisely, but I'll use the definition from Nonlinear Dynamics and Chaos by S.H. Strogatz to show the features everyone agrees on: Chaos is aperiodic long-term behaviour in a deterministic system that exhibits sensitive dependence on initial conditions. Aperiodic long-term behaviour means there are no fixed points, closed ...


4

What's $V_{ab}$? Well, $V_{ab}$ is the "symmetric, positive definite potential energy matrix". Ok lol I'm trolling here, but as the name suggests, $V_{ab}$ describes the strength of the (linearized) interaction between particles $a$ and $b$. To be precise, it is the second derivative of the potential energy function of the system with respect to $u_a$ and $...


4

We are considering a discrete time evolution $$x_{n}~=~f(x_{n-1})~=~f^{n\circ}(x_0), \qquad n~\in~\mathbb{N},$$ in a $2N$-dimensional symplectic manifold $(M,\omega)$, where $f$ is a symplectomorphism. Let us for simplicity work in local coordinates. Define the Jacobian matrix as $$\tag{1} A(x,n)^{i}{}_{j}~:=~\frac{\partial (f^{n\circ} (x))^i}{\partial x^j}....


4

He's saying "look in the mirror" - what you see is yourself, and you are more than a simple pile of atoms. He is introducing some ideas from complexity theory by means of repetition of scenarios, and self-reflection, pun intended (by Feynman).


3

First, you need a deterministic dynamical system. By deterministic it means that the state of the system is univocally determined at each time, ie. at each time you have one and only one possible state. In the counterpart are stochastic systems where, instead, the state of the system is determined by a distribution of possible states and is this distribution ...


3

According to Nonlinear Dynamics and Chaos by Steven Strogatz The requirements for chaos are: Deterministic system (only one future for each state) Irregular spatial, temporal, or spatiotemporal patterns (a qualitative feature) A positive maximum Lyapunov exponent. 3) is pretty much the quantitative standard in journals of chaos, assuming you meet the ...


3

No, I would say it is wrong to immediately conclude that there is no scale just because the variance diverges. Only functions of $x$ of the form $x^n$, a power law, have a chance to be considered scale-free; none of these functions may be considered a probability distribution because the integral diverges. Any other function – and therefore any normalizable ...


3

Maybe these three lectures about emergence could be interesting to get a first overview of the topic. Therein Prof. De Deo explains for example that emergence has a lot to to with what new phenomena can occurre when coarse graining (or renormalizing) microscopic degrees of freedom of a large system to obtain an effective (possibly including emergent ...


3

The roll off deviation appears to mostly be due to difficulties with accurate measurements at low magnitudes. In order to preserve the GR law you'd need to exhaustively record all earthquake measurements below the roll off magnitude and this is largely infeasible. A good example to look at (figure 3.1) is the difference between the Sumatra 2004 and Kobe ...


3

The critical point of a general statistical system is a point in the space parameterized by intensive quantities, especially temperature and pressure, at which there exist no boundaries between two different phases of the material even though the boundaries exist at an infinitesimally nearby point. It's the end of a co-existence curve for two phases. Outside ...


3

This answer contains some additional resources that may be useful. Please note that answers which simply list resources but provide no details are strongly discouraged by the site's policy on resource recommendation questions. This answer is left here to contain additional links that do not yet have commentary. Uriel Frisch, Turbulence: the legacy of A.N. ...


3

It a widely known and experimentally useful fact in nuclear and particle physics that the position and momentum distributions of bound systems are related to one another by a Fourier transform. Is the system you are inspecting bound? The tails in the data that Nathaniel notes suggest that it is not fully bound, which means the Fourier relationship between ...


3

I take the core of the question to be Is it possible to do linear stability analysis on 2nd order differential equations by finding eigen values of Jacobian matrix? The answer is yes, but first you have to convert your second-order equations into first-order ones. This is actually pretty easy to do: every time you see a second derivative, e.g. $x''$,...


3

$f=-kx$ is stable whereas $f=kx$ is unstable. You can usually rewrite a matrix $A$ as $A=PDP^{-1}$ where $P$ is a matrix of eigenvectors and $D$ is a diagonal matrix of eigenvalues. If $F=Ax$, then by the above, $(P^{-1}F)=D(P^{-1}x)$. Now you have $n$ independent equations exactly of the form $f=kx$ or $f=-kx$. If any one of them is like $f=kx$, the ...


3

Let us look at a one-dimensional example: Recall that $f(x) = \dot{x}$, so $f$ encodes the time evolution of $x$. If $f < 0$, then $x$ will move to the left. If $f > 0$, then $x$ will move to the right. If $f = 0$, $x$ will not move at all, this is why $f(x_0) = 0$ is the equilibrium condition. Now, look what happens if you perturb the equilibria $...


3

Will there be a symbolic sequence for each dimension or will a symbol be assigned to a point $(x,y)$? This depends what you eventually want to do with your symbol sequence, but for typical applications, such as determining the entropy or modelling, you want to assign one symbol to the point. The general reason behind this is that (for a proper ...


3

One always needs to allow the frequency to change, otherwise one gets horrible secular terms. In case of resonances one needs additional tricks. A good mathematical book is ''Perturbation methods in nonlinear systems'' by G.E.O. Giacaglia (Springer 2012). He discusses both the traditional Poincare-Linsted method and more advanced methods based on Lie ...


3

The correct keyword for this is "renormalization". And, as you said yourself, high energy physics shares this field with condensed matter. In condensed matter, you always encounter renormalization group of some kind if you are interested in critical phase transitions (which are scale invariant). Good references for this are Wikipedia and this book. ...


2

If x(t) is a random process it is quite unlikely that the derivative xdot(t) exists. So your description looks somewhat problematic. It seems that you have a Wiener process (= random walk, Brownian motion). See http://en.wikipedia.org/wiki/Wiener_process Here the changes in x are Gaussian and uncorrelated with x itself. Then x itself also follows a ...



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