Mathematics. World. Randomness.

Brownian motion describes the random movement of particles in fluids, however, this revolutionary model only works when a fluid is static, or at equilibrium. In real-life environments, fluids often contain particles that move by themselves, such as tiny swimming microorganisms. These self-propelled swimmers can cause movement or stirring in the fluid, which drives it away from equilibrium.

Researchers from Queen Mary University of London, Tsukuba University, École Polytechnique Fédérale de Lausanne and Imperial College London, have presented a novel theory to explain observed particle movements in these dynamic environments.

By explicitly solving the scattering dynamics between the passive particle and active swimmers in the fluid, the researchers were able to derive an effective model for particle motion in ‘active’ fluids, which accounts for all experimental observations.

Their extensive calculation reveals that the effective particle dynamics follow a so-called ‘Lévy flight’, which is widely used to describe ‘extreme’ movements in complex systems that are very far from typical behaviour.

Dr Kiyoshi Kanazawa from the University of Tsukuba, and first author of the study, said: “So far there has been no explanation how Lévy flights can actually occur based on microscopic interactions that obey physical laws. Our results show that Lévy flights can arise as a consequence of the hydrodynamic interactions between the active swimmers and the passive particle, which is very surprising.” (1)

Random movements.

In a random world.

Particles.

Observed by a human.

Under the Sun.

Shining bright.

Observed by the Galaxy.

Moving fast.

Under the void.

Look.

Particle moving.

Human watching.

The Sun setting.

Galaxy standing still.

Universe dying.

God looking…

Random movements…

Classical physics. QM. Intuitionism.

Photo by Spiros Kakos from Pexels

In classical physics, it is accepted that everything has already been determined since the Big Bang. To explore the future of our cosmos, physicists employ the language of classical mathematics and represent the universe’s initial conditions conditions by real numbers. “These numbers are characterized by an infinite number of decimals that follow the dot,” says professor Nicolas Gisin. There is a problem, however: given that our world is finite, how can it include numbers that are infinite and that feature an infinite amount of information?

To circumvent this paradox, professor Gisin suggests the use of a different language. “There is another mathematical language, called intuitionistic, which doesn’t believe in the existence of the infinite,” continues the Geneva physicist. Instead of real numbers containing an infinite number of decimals at a given moment, intuitionistic mathematics represents these numbers as a random process that takes place over time, one decimal after the other, so that at each given moment, there is only a finite number of decimals.

(Note: There is another difference between the two mathematical languages: the truth of propositions. In intuitionistic mathematics, a proposition is either true, false or indeterminate) (1)

A cosmos full of information.

A world governed by laws.

An infinite universe.

Filled with humans.

An empty cosmos.

A cosmos with no laws.

A finite cosmos.

Filled with death.

A cosmos void of any information…

A cosmos full of infinite wisdom…

Don’t be perplexed.

There is no answer to your questions.

And that is the answer.

Now move on.

Step by step.

Can you listen to Him walking?

Why doesn’t any animal have three legs?

Photo by Spiros Kakos from Pexels

If ‘Why?’ is the first question in science, ‘Why not?’ must be a close second. Sometimes it’s worth thinking about why something does not exist. Such as a truly three-legged animal. At least one researcher has been pondering the non-existence of tripeds.

“Almost all animals are bilateral,” he said. The code for having two sides to everything seems to have got embedded in our DNA very early in the evolution of life — perhaps before appendages like legs, fins or flippers even evolved. Once that trait for bilateral symmetry was baked in, it was hard to change.

With our built-in bias to two-handedness, it can be hard to figure out how a truly three-legged animal would work — although that has not stopped science fiction writers from imagining them. Perhaps trilateral life has evolved on Enceladus or Alpha Centauri (or Mars!) and has as much difficulty thinking about two-limbed locomotion as we do thinking about three.

This kind of thought experiment is useful for developing our ideas about evolution, Thomson said. (1)

How fascinating.

Everything started with Nothing.

Then One came into existence.

We are still in the phase of Two…

And there is no way to get any further.

For going further means that we get to three.

And from there infinity is one step away.

Leading to nothing more than zero once again…

But there is no infinity.

There is no two.

Not even One.

For only everything exists.

Infinity!

In the palm of a small kid…

Chaos. Numbers. Simulations.

Photo by Spiros Kakos from Pexels

Digital computers use numbers based on flawed representations of real numbers, which may lead to inaccuracies when simulating the motion of molecules, weather systems and fluids, find scientists.

The study, published today in Advanced Theory and Simulations, shows that digital computers cannot reliably reproduce the behaviour of ‘chaotic systems’ which are widespread. This fundamental limitation could have implications for high performance computation (HPC) and for applications of machine learning to HPC.

Professor Peter Coveney, Director of the UCL Centre for Computational Science and study co-author, said: “Our work shows that the behaviour of the chaotic dynamical systems is richer than any digital computer can capture. Chaos is more commonplace than many people may realise and even for very simple chaotic systems, numbers used by digital computers can lead to errors that are not obvious but can have a big impact. Ultimately, computers can’t simulate everything.”

The team investigated the impact of using floating-point arithmetic — a method standardised by the IEEE and used since the 1950s to approximate real numbers on digital computers.

Digital computers use only rational numbers, ones that can be expressed as fractions. Moreover the denominator of these fractions must be a power of two, such as 2, 4, 8, 16, etc. There are infinitely more real numbers that cannot be expressed this way. (https://www.sciencedaily.com/releases/2019/09/190923213314.htm)

An irrational universe.

Full of irrational people.

Trying to analyze it rationally.

Under the illusion that number we have invented can draw a sketch of the cosmos. And yet, nothing we have invented is anywhere to be seen but on a piece of paper. Can you limit the birth of a star on a piece of paper? Can you contain the death of the universe on an equation?

We believe we can.

And sadly, we do.

And at the moment we do, the universe indeed dies…

And a small voice will whisper in our ear…

Congratulations. You have now understood it all.

How irrationally rational everything is!

And inside the darkest night you will dance.

Laughter.

And for a brief moment the forest will look at you.

Crying.

And for a brief moment the forest will see nothing…

But an empty broken CD. Full of data. Full of life…

Old mathematics… Broken cosmos… Blurry image…

Photo by Spiros Kakos from Pexels

By combining cutting-edge machine learning with 19th-century mathematics, a Worcester Polytechnic Institute (WPI) mathematician worked to make NASA spacecraft lighter and more damage tolerant by developing methods to detect imperfections in carbon nanomaterials used to make composite rocket fuel tanks and other spacecraft structures.

Using machine learning, neural networks, and an old mathematical equation, Randy Paffenroth has developed an algorithm that will significantly enhance the resolution of density scanning systems that are used to detect flaws in carbon nanotube materials.

The algorithm was “trained” on thousands of sets of nanomaterial images and to make it more effective at making a high-resolution image out of a low-resolution image, he combined it with the Fourier Transform, a mathematical tool devised in the early 1800s that can be used to break down an image into its individual components.

“The Fourier Transform makes creating a high-resolution image a much easier problem by breaking down the data that makes up the image. Think of the Fourier Transform as a set of eyeglasses for the neural network. It makes blurry things clear to the algorithm. We’re taking computer vision and virtually putting glasses on it”, said Paffenroth. (1)

We like breaking the world into pieces.

We can see better that way.

But even the sharpest image of a tree.

Conveys nothing about the forest…

A forest that is there because of the trees.

Trees we know are there.

We remember those trees.

We once saw those trees.

Casting their shadows during the evening hours.

At a time when we used to stand within a forest.

But never really saw one…

Cause in the midst of the evening.

There was nothing else casting a shadow.

Nothing but our self!

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