Evidence of the square root of Brownian motion

06/03/2014

ResearchBlogging.org

A mathematical proof of existence of a stochastic process involving fractional exponents seemed out of question after some mathematicians claimed this cannot exist. This observation is strongly linked to the current definition and may undergo revision if nature does not agree with it. Stochastic processes are very easy to simulate on a computer. Very few lines of code can decide if something works or not. I and Alfonso Farina, together with Matteo Sedehi,  have introduced the idea that the square root of a Wiener process yields the Schroedinger equation (see here or download a preprint here). This implies that one has to attach a meaning to the equation

dX=(dW)^\frac{1}{2}.

In a paper appeared today on arxiv (see here) we finally have provided this proof: We were right. The idea is to solve such an equation by numerical methods. These methods are themselves a proof of existence. We used the Euler-Maruyama method, the simplest one and we compared the results as shown in the following figure

a) Original Brownian motion. b) Same but squaring the formula for the square root. c) Formula of the square root taken as a stochastic equation. d)  Same from the stochastic equation in this post.

a) Original Brownian motion. b) Same but squaring the formula for the square root. c) Formula of the square root taken as a stochastic equation. d) Same from the stochastic equation in this post.

There is now way to distinguish each other and the original Brownian motion is completely recovered by taking the square of the square root process computed in three different ways. Each one of these completely supports the conclusions we have drawn in our published paper. You can find the code to recover this figure in our arxiv paper. It is obtained by a Monte Carlo simulation with 10000 independent paths. You can play with it changing the parameters as you like.

This paper has an important consequence: Our current mathematical understanding of stochastic processes should be properly extended to account for our results. As a by-product, we have shown how, using Pauli matrices, this idea can be generalized to include spin introducing a new class of stochastic processes in a Clifford algebra.

In conclusion, we would like to remember that, it does not matter what your mathematical definition could be, a stochastic process is always a well-defined entity on a numerical ground. Tests can be easily performed as we proved here.

Farina, A., Frasca, M., & Sedehi, M. (2013). Solving Schrödinger equation via Tartaglia/Pascal triangle: a possible link between stochastic processing and quantum mechanics Signal, Image and Video Processing, 8 (1), 27-37 DOI: 10.1007/s11760-013-0473-y

Marco Frasca, & Alfonso Farina (2014). Numerical proof of existence of fractional Wiener processes arXiv arXiv: 1403.1075v1


Tartaglia-Pascal triangle and quantum mechanics

26/04/2013

ResearchBlogging.org

The paper I wrote with Alfonso Farina and Matteo Sedehi about the link between the Tartaglia-Pascal triangle and quantum mechanics is now online (see here). This paper contains as a statement my theorem that provides a connection between the square root of a Wiener process and the Schrödinger equation that arose a lot of interest and much criticisms by some mathematicians (see here). So, it is worthwhile to tell how all this come about.

On fall 2011, Alfonso Farina called me as he had an open problem after he and his colleagues got published a paper on Signal, Image and Video Processing, a journal from Springer, where it was shown how the Tartaglia-Pascal triangle is deeply connected with diffusion and the Fourier equation. Tartaglia-Pascal triangleThe connection comes out from the Joseph Fourierbinomial coefficients, the elements of the Tartaglia-Pascal triangle, that in some limit give a Gaussian and this Gaussian, in the continuum, is the solution of the Fourier equation of heat diffusion. This entails a deep connection with stochastic processes. Stochastic processes, for most people working in the area of radar and sensors, are essential to understand how these device measure through filtering theory. But, in the historic perspective Farina & al. put their paper, they were not able to get a proper connection for the Schrödinger equation, notwithstanding they recognized there is a deep formal analogy with the Fourier equation. This was the open question: How to connect Tartaglia-Pascal triangle and Schrödinger equation?

People working in quantum physics are aware of the difficulties researchers have met to link stochastic processes a la Wiener and quantum mechanics. Indeed, skepticism is the main feeling of all of us about this matter. So, the question Alfonso put forward to me was not that easy. But Alfonso & al. paper contains also a possible answer: Just start from discrete and then go back to continuum. So, the analog of the heat equation is the Schrödinger equation for a free particle and its kernel and, indeed, the evolution of a Gaussian wave-packet can be managed on the discrete and gives back the binomial coefficient. What you get in this way are the square root of binomial coefficients. Erwin SchrödingerSo, the link with the Tartaglia-Pascal triangle is rather subtle in quantum mechanics and enters through a square root, reminiscent of the Dirac’s work and his greatest achievement, Dirac equation. This answered Alfonso’s question and in a way that was somewhat unexpected.

Then, I thought that this connection could be deeper than what we had found. I tried to modify Itō calculus to consider fractional powers of a Wiener process. I posted my paper on arxiv and performed both experimental and numerical computations. All this confirms my theorem that the square root of a Wiener process has as a diffusion equation the Schrödinger equation. You can easily take the square root of a natural noise (I did it) or compute this on your preferred math software. It is just interesting that mathematicians never decided to cope with this and still claim that all this evidence does not exist, basing their claims on a theory that can be easily amended.

We have just thrown a seed in the earth. This is our main work. And we feel sure that very good fruits will come out. Thank you very much Alfonso and Matteo!

Farina, A., Frasca, M., & Sedehi, M. (2013). Solving Schrödinger equation via Tartaglia/Pascal triangle: a possible link between stochastic processing and quantum mechanics Signal, Image and Video Processing DOI: 10.1007/s11760-013-0473-y

Marco Frasca (2012). Quantum mechanics is the square root of a stochastic process arXiv arXiv: 1201.5091v2

Farina, A., Giompapa, S., Graziano, A., Liburdi, A., Ravanelli, M., & Zirilli, F. (2011). Tartaglia-Pascal’s triangle: a historical perspective with applications Signal, Image and Video Processing, 7 (1), 173-188 DOI: 10.1007/s11760-011-0228-6


Fooling with mathematicians

28/02/2013

ResearchBlogging.org

I am still working with stochastic processes and, as my readers know, I have proposed a new view of quantum mechanics assuming that at the square root of a Wiener process can be attached a meaning (see here and here). I was able to generate it through a numerical code. A square root of a number can always be taken, irrespective of any deep and beautiful mathematical analysis. The reason is that this is something really new and deserves a different approach much in the same way it happened to the Dirac’s delta that initially met with skepticism from the mathematical community (simply it did not make sense with the knowledge of the time). Here I give you some Matlab code if you want to try by yourselves:

nstep = 500000;
dt = 50;
t=0:dt/nstep:dt;
B = normrnd(0,sqrt(dt/nstep),1,nstep);
dB = cumsum(B);
% Square root of the Brownian motion
dB05=(dB).^(1/2);

Nothing can prevent you from taking the square root of  a number as is a Brownian displacement and so all this has a well sound meaning numerically. The point is just to understand how to give this a full mathematical meaning. The wrong approach in this case is just to throw all away claiming all this does not exist. This is exactly the behavior I met from Didier Piau. Of course, Didier is a good mathematician but simply refuses to accept the possibility that such concepts can have a meaning at all based on what has been so far coded in the area of stochastic processes. This notwithstanding that they can be easily computed on your personal computer at home.

But this saga is not over yet. This time I was trying to compute the cubic root of a Wiener process and I posted this at Mathematics Stackexchange. I put this question with  the simple idea in mind to consider a stochastic process with a random mean and I did not realize that I was provoking a small crisis again. This time the question is the existence of the process {\rm sign}(dW). Didier Piau immediately wrote down that it does not exist. Again I give here the Matlab code that computes it very easily:

nstep = 500000;
dt = 50;
t=0:dt/nstep:dt;
B = normrnd(0,sqrt(dt/nstep),1,nstep);
dB = cumsum(B);
% Sign and absolute value of a Wiener process
dS = sign(dB);
dA = dB./dS;

Didier Piau and a colleague of him just complain on the Matlab way the sign operation is performed. My view is that it is all legal as Matlab takes + or – depending on the sign of the displacement, a thing that can be made by hand and that does not imply anything exotic.  What it is exotic here it the strong opposition this evidence meets notwithstanding is easily understandable by everybody and, of course, easily computable on a tabletop computer. The expected distribution for the signs of Brownian displacements is a Bernoulli with p=1/2. Here is the histogram from the above code

Histogram sign(dW)This has mean 0 and variance 1 as it should for N=\pm 1 and p=\frac{1}{2} but this can be verified after some Montecarlo runs. This is in agreement with what I discussed here at Mathematics Stackexchange as a displacement in a Brownian motion is a physics increment or decrement of the moving particle and has a sign that can be managed statistically. My attempt to compare all this to the case of Dirac’s delta turns out into a complain of overstatement as delta was really useful and my approach is not (but when Dirac put forward his idea this was just airy-fairy for the time). Of course, a reformulation of quantum mechanics would be a rather formidable support to all this but this mathematician does not seem to realize it.

So, in the end, I am somewhat surprised by the behavior of the community against novelties. I can understand skepticism, it belongs to our profession, but for facing new concepts that can be easily checked numerically to exist I would prefer a more constructive behavior trying to understand rather than an immediate dismissal. It appears like history of science never taught anything leaving us with a boring repetition of stereotyped reactions to something that instead would be worthwhile further consideration. Meanwhile, I hope my readers will enjoy playing around with these new computations using some exotic mathematical operations on a stochastic process.

Marco Frasca (2012). Quantum mechanics is the square root of a stochastic process arXiv arXiv: 1201.5091v2


A first paper on square root of a Brownian motion and quantum mechanics gets published!

20/11/2012

ResearchBlogging.org

Following my series of posts on the link between the square root of a stochastic process and quantum mechanics (see here, here, here, here, here), that I proved to exist both theoretically and experimentally, I am pleased to let you know that the first paper of my collaboration with Alfonso Farina and Matteo Sedehi was finally accepted in Signal, Image and Video Processing. This paper contains the proof of what I named the “Farina-Frasca-Sedehi proposition” in my paper that claims that for a well localized free particle there exists a map between the wave function and the square root of binomial coefficients. This finally links the Pascal-Tartaglia triangle, given through binomial coefficients, to quantum mechanics and closes a question originally open by Farina and collaborators on the same journal (see here). My theorem about the square root of a stochastic process also appears in this article but without a proof.

Marco Frasca (2012). Quantum mechanics is the square root of a stochastic process arXiv arXiv: 1201.5091v2

Farina, A., Giompapa, S., Graziano, A., Liburdi, A., Ravanelli, M., & Zirilli, F. (2011). Tartaglia-Pascal’s triangle: a historical perspective with applications Signal, Image and Video Processing DOI: 10.1007/s11760-011-0228-6


Johnson noise and its square root: The video

28/03/2012

We have uploaded the video of the measurement of the square root of Johnson noise:

This will participate to the Google Science Fair 2012.


Johnson noise and its square root

23/03/2012

ResearchBlogging.org

Following my recent work on stochastic processes and quantum mechanics (see here and here), after I showed its existence with numerical computation (see here), this time I moved one step forward with an experimental setup. The idea come out from my son Giorgio. He is a teen with a lot of ideas and was of real help to assemble all the  apparatus.

In order to try to see how my mathematics for stochastic processes could be applied to a real stochastic motion I needed a natural source to start with. The best and easier to manage of this is Johnson-Nyquist thermal noise in a resistance. This effect is really small being of the order of nV and so we were in need for some significant instrumentation. To buy it was impossible as this is generally very highly priced and not affordable for amateur efforts. There is some hope from ebay to find some old apparatus but one is not granted to have it in fully order and, in any case, this should come from US and so one have to incur in a lot of problems, not least time to deliver, that make us desist. The next step was to find in the known literature a possible circuit for a very low noise and high gain preamaplier that we could realize just buying components. Looking around with google I have found this paper by Graziella Scandurra, Gianluca Cannatà, and Carmine Ciofi of University of Messina in Italy. I wrote to Dr. Scandurra to ask some clarifications and she was so kind to pointing me out toward another circuit that fitted better my needs, similar to the one I have found, published in this paper. The circuit given by the same authors in the latter is used in the former but in a differential topology. For my aims, a differential preamplifier was not needed and so I followed the advice by Dr. Scandurra and opted for the simplest circuital solution. Unfortunately, this circuit used a IF3602 by Interfet. This is a dual N-channel jfet in a single package having a very low noise figure and high gain. This component is practically impossible to find and the only way to get is to order it through some reseller with a large number of items at a high price. Not the best for me. So, I was in need for a substitute. A colleague of mine, Massimiliano Rossi (former researcher at Tor Vergata University in Rome) pointed me out the Toshiba jfet 2SK170BL normally used in audio applications. Looking at the datasheet this appears well suited to substitute the IF3602 but this comes singly packaged. I solved this issue by buying a matched quad on ebay and using a pair of them.

Last week-end, with the help of my son, I have built the circuit as given in the paper and provided the given substitution. This was the result with the circuit perfectly working

and here with the output given an input signal from a BF generator

As you can see it is up and running. This circuit, as designed by the researchers of University of Messina, proved to be robust and reliable. So, here is my first measurement using as a device under test a potentiometer with a maximum of 1 MOhm:

and this is the final proof that this is indeed Johnson noise

giving the experimental proof of existence for the square root of a stochastic process.

As a final aside, I give here the circuit of the preamplifier with the list of components for those aiming to realize it:

and

As said above, you can substitute the IF3602 with a matched pair of 2SK170BL by Toshiba at very low cost.

So, we can conclude that the square root of a stochastic process, as given in my paper, exists and is meaningful both numerically and experimentally.

Marco Frasca (2012). Quantum mechanics is the square root of a stochastic process arXiv arXiv: 1201.5091v2

Scandurra, G., Cannatà, G., & Ciofi, C. (2011). Differential ultra low noise amplifier for low frequency noise measurements AIP Advances, 1 (2) DOI: 10.1063/1.3605716

Cannatà, G., Scandurra, G., & Ciofi, C. (2009). An ultralow noise preamplifier for low frequency noise measurements Review of Scientific Instruments, 80 (11) DOI: 10.1063/1.3258197


Numerical evidence for the square root of a Wiener process

02/02/2012

ResearchBlogging.org

Brownian motion is a very kind mathematical object being very keen to numerical simulations. There are a plenty of them for any platform and software so that one is able to check very rapidly the proper working of a given hypothesis. For these aims, I have found very helpful the demonstration site by Wolfram and specifically this program by Andrzej Kozlowski. Andrzej gives the code to simulate Brownian motion and compute Itō integral to verify Itō lemma. This was a very good chance to check my theorems recently given here by some numerical work. So, I have written a simple code on Matlab that I give here (rename from .doc to .m to use with Matlab).

Here is a sample of output:

As you could note, the agreement is almost perfect. I have had to rescale with a multiplicative factor as the square root appears somewhat magnified after the square but the pattern is there. You can do checks by yourselves. So, all my equations are perfectly defined as is a possible square root of a Wiener process.

Of course, improvements, advices or criticisms are very welcome.

Update: I have simplified the code and added a fixed scale factor to make identical scale. The code is available at Simulation. Here is an example of output:

Marco Frasca (2012). Quantum mechanics is the square root of a stochastic process arXiv arXiv: 1201.5091v2


Follow

Get every new post delivered to your Inbox.

Join 70 other followers

%d bloggers like this: