A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the [super-heated ] plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars.
From:
DOE/Princeton Plasma Physics Laboratory
February
12, 2021 -- The algorithm, devised by a scientist at the U.S. Department of
Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL), applies machine
learning, the form of artificial intelligence (AI) that learns from experience,
to develop the predictions. "Usually in physics, you make observations,
create a theory based on those observations, and then use that theory to
predict new observations," said PPPL physicist Hong Qin, author of a paper
detailing the concept in Scientific Reports. "What I'm doing
is replacing this process with a type of black box that can produce accurate
predictions without using a traditional theory or law."
Qin
(pronounced Chin) created a computer program into which he fed data from past observations
of the orbits of Mercury, Venus, Earth, Mars, Jupiter, and the dwarf planet
Ceres. This program, along with an additional program known as a "serving
algorithm," then made accurate predictions of the orbits of other planets
in the solar system without using Newton's laws of motion and gravitation.
"Essentially, I bypassed all the fundamental ingredients of physics. I go
directly from data to data," Qin said. "There is no law of physics in
the middle."
The program does not happen upon
accurate predictions by accident. "Hong taught the program the underlying
principle used by nature to determine the dynamics of any physical
system," said Joshua Burby, a physicist at the DOE's Los Alamos National
Laboratory who earned his Ph.D. at Princeton under Qin's mentorship. "The
payoff is that the network learns the laws of planetary motion after witnessing
very few training examples. In other words, his code really 'learns' the laws
of physics."
Machine learning is what makes computer
programs like Google Translate possible. Google Translate sifts through a vast
amount of information to determine how frequently one word in one language has
been translated into a word in the other language. In this way, the program can
make an accurate translation without actually learning either language.
The process also appears in
philosophical thought experiments like John Searle's Chinese Room. In that
scenario, a person who did not know Chinese could nevertheless
"translate" a Chinese sentence into English or any other language by
using a set of instructions, or rules, that would substitute for understanding.
The thought experiment raises questions about what, at root, it means to
understand anything at all, and whether understanding implies that something
else is happening in the mind besides following rules.
Qin was inspired in part by Oxford
philosopher Nick Bostrom's philosophical thought experiment that the universe
is a computer simulation. If that were true, then fundamental physical laws
should reveal that the universe consists of individual chunks of space-time,
like pixels in a video game. "If we live in a simulation, our world has to
be discrete," Qin said. The black box technique Qin devised does not
require that physicists believe the simulation conjecture literally, though it builds
on this idea to create a program that makes accurate physical predictions.
The resulting pixelated view of the
world, akin to what is portrayed in the movie The Matrix, is known as a
discrete field theory, which views the universe as composed of individual bits
and differs from the theories that people normally create. While scientists
typically devise overarching concepts of how the physical world behaves,
computers just assemble a collection of data points.
Qin and Eric Palmerduca, a graduate
student in the Princeton University Program in Plasma Physics, are now
developing ways to use discrete field theories to predict the behavior of
particles of plasma in fusion experiments conducted by scientists around the
world. The most widely used fusion facilities are doughnut-shaped tokamaks that
confine the plasma in powerful magnetic fields.
Fusion, the power that drives the sun
and stars, combines light elements in the form of plasma -- the hot, charged
state of matter composed of free electrons and atomic nuclei that represents
99% of the visible universe -- to generate massive amounts of energy.
Scientists are seeking to replicate fusion on Earth for a virtually
inexhaustible supply of power to generate electricity.
"In a magnetic fusion device, the dynamics of plasmas are
complex and multi-scale, and the effective governing laws or computational
models for a particular physical process that we are interested in are not
always clear," Qin said. "In
these scenarios, we can apply the machine learning technique that I developed
to create a discrete field theory and then apply this discrete field theory to
understand and predict new experimental observations."
This process opens up questions about
the nature of science itself. Don't scientists want to develop physics theories
that explain the world, instead of simply amassing data? Aren't theories
fundamental to physics and necessary to explain and understand phenomena?
"I would argue that the ultimate
goal of any scientist is prediction," Qin said. "You might not
necessarily need a law. For example, if I can perfectly predict a planetary
orbit, I don't need to know Newton's laws of gravitation and motion. You could
argue that by doing so you would understand less than if you knew Newton's
laws. In a sense, that is correct. But from a practical point of view, making
accurate predictions is not doing anything less."
Machine learning could also open up
possibilities for more research. "It significantly broadens the scope of
problems that you can tackle because all you need to get going is data,"
Palmerduca said.
The technique could also lead to the
development of a traditional physical theory. "While in some sense this
method precludes the need of such a theory, it can also be viewed as a path
toward one," Palmerduca said. "When you're trying to deduce a theory,
you'd like to have as much data at your disposal as possible. If you're given
some data, you can use machine learning to fill in gaps in that data or
otherwise expand the data set."
Support for this research came from the
DOE Office of Science (Fusion Energy Sciences).
https://www.sciencedaily.com/releases/2021/02/210212094120.htm
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