As an important branch of natural sciences, physics studies fundamental laws and phenomena such as matter, energy, mechanics and motion, thus providing an important theoretical basis for human beings to understand and explore the natural world. To be precise, physics models nature mathematically.
With the
advancement of science and technology and the fast development of Artificial
Intelligence, physics is facing new challenges and opportunities. The AI
application is changing the research methods and development trajectory of
physics, thus offering new possibilities for progress and innovation.
Artificial
Intelligence can help physicists to build more accurate and complex models and
to analyse and interpret experiments and data provided by observation. We must
keep in mind algorithms such as machine learning, of which deep learning is a
part.
The
difference lies in the fact that deep learning is more advanced: a deep
learning algorithm is not conditioned by the user’s experience. Just to make an
example, in non-deep machine learning, to distinguish cats and dogs you have to
tell “do it by ears, hair, etc…”, while in deep learning the distinguishing
features are extracted by the code itself and, often or always, they are
actually patterns that we humans would never be able to have!
It does
this in the following way: you give it a set of training data and the expected
results. The algorithm starts to do tests on this recognition until it reaches
an acceptable accuracy value based on what it should come up with by using
iterative mathematics (and obviously there is the human hand in the
construction of the algorithm). When it has “adjusted”, you can use it on
unknown pictures of cats and dogs, not used for learning, so that it classifies
them to the human without the human having to do it himself/herself.
Considering the above, Artificial Intelligence can discover hidden patterns and
correlations from large amounts of data, thus helping physicists to understand
and predict related phenomena.
Artificial
Intelligence can be applied to theoretical physics and computational physics
research to improve the efficiency and accuracy of computational models and
methods. For example, Artificial Intelligence can help physicists develop
numerical simulation methods since machine learning is not only for
classification, but also for numerical prediction, which is especially useful
in the financial field, as it is more efficient at speeding up experiments and
calculations.
Artificial
Intelligence also has broad applications in the fields of quantum physics and
quantum computing. Quantum physics is a branch of science that studies the
behaviour of microscopic particles and the laws of quantum mechanics, while
quantum computing is an emerging field that utilises the characteristics of
quantum mechanics for information processing and calculations. Artificial
Intelligence can help physicists design more complex quantum systems and
algorithms and promote the development and application of computer science.
The AI
application in high-energy physics and particle physics experiments is also
very important. High-energy physics studies the structure and interaction of
microscopic particles, while particle physics studies the origin and evolution
of the universe. Artificial Intelligence can help physicists analyse and
process large amounts of experimental data and discover potential new particles
and physical phenomena.
Al
technology can improve the efficiency of physics research and accelerate the
scientific research process. Physics research often requires large amounts of
experimental data and complex computational models, and Artificial Intelligence
can streamline the work of physicists in discovering hidden patterns and
correlations in this data. Artificial Intelligence can also provide more
accurate and detailed physics models, helping physicists solve even more
complex scientific problems.
Traditional
physics research often relies on existing theories and experiments, while
Artificial Intelligence can help physicists discover new phenomena and physics
laws. By bringing to light patterns and correlations from large amounts of
data, Artificial Intelligence stimulates physicists to propose new hypotheses
and theories, thus promoting development and innovation.
The AI
application explores unknown fields and phenomena. By analysing and extracting
information from large amounts of data, Artificial Intelligence expands the
scope and depth of physics research.
The
development of Artificial Intelligence offers new opportunities for the integration
of physics with other disciplines. For example, the combination of Artificial
Intelligence and biological sciences can help physicists study complex
biological systems and related phenomena. The combination of Artificial
Intelligence and chemistry can help physicists study molecular structure and
chemical reactions.
Although
AI technology has broad application prospects in physics research, it also has
to face some challenges including the acquisition and processing of data as
this is the main problem, especially when dealing with new issues for which
databases are scarce; the creation and verification of the physical model; and
the selection and optimisation of algorithms. In this regard, it must be said
that the boom in deep learning has mainly been due to the increase in available
data thanks to the Internet and the advancement of hardware. The networks that
anyone uses can run on their laptops, albeit slowly, but this would have been
unthinkable in the 1990s, when deep learning was already being thought of in a
very vague way. It is not for nothing that we speak of the “democratisation of
deep learning”.
Future
development requires cooperation and exchanges between physicists and AI
professionals to jointly resolve these challenges and better apply this new
technology to physics research and applications.
As an
emerging technology, Artificial Intelligence is revolutionising traditional
physics. By applying Artificial Intelligence, physicists can build more
accurate and complex models, analyse and explain physics experiments and
observational data. Artificial Intelligence necessarily accelerates the
research process in physics and promote the development and innovation of
so-called traditional physics.
Artificial
Intelligence, however, still has to face some challenges and problems in
physics research, which require further study and exploration. In the future,
AI technology will be further utilised in physics research and applications,
thus providing more opportunities and challenges for development and
innovation.
AI
technology is also used in gravitational wave research, whose 2017 Nobel Prize
in Physics was awarded to Rainer Weiss (Germany), Barry C. Barish (USA) and Kip
S. Thorne (USA).
On 14
September 2015 this group of scientists detected the gravitational wave signal
of a system of two black holes merging for the first time. At that moment, it
triggered a revolution in the astrophysics community: the research group
involved in the discovery of gravitational waves was listed as a candidate for
the Nobel Prize in Physics ever since.
The two
black holes are located about 1.8 billion light years from Earth. Their masses
before the merger were equivalent to 31 and 25 suns in size, respectively.
After the merger, the total mass was equivalent to 53 suns in size. Three suns
were converted into energy and released in the form of gravitational waves.
For some
time, gravitational waves have attracted the attention and curiosity not only
of scientists, but also of ordinary citizens. Despite being a weak force – a
child lifting a toy amply demonstrates this – gravitational interaction has
always created questions: but what are gravitational waves?
To put
it simply and briefly, this concept of gravitational waves comes from
Einstein’s theory of general relativity. We all know that the theory of
relativity always discusses the dialectical relationship between space-time and
matter, and the viewpoint of gravitational waves is that matter causes ripples
and bends into space-time. The curve propagates outwards from the radiation
source in the form of a wave. This wave transmits energy as gravitational
radiation and the speed of gravitational waves is close to that of light. An
extreme case is a black hole. Its supermass causes a distortion of space-time;
light cannot escape and slips into it.
Because
our basic understanding of traditional physics is based on Newton’s theory of
universal gravitation, it is assumed that all objects have a mutual attraction.
The size of this force is proportional to the mass of each object. Einstein
believed this theory to be superficial. The reason for what appears to be the
effect of gravity is due to the distortion of space and time. Hence, if
Newton’s law of universal gravitation is approximate, is our current knowledge
based on traditional physics going astray? The question is an awkward one.
Hence let us leave it to scientists to further study who is right and who is
wrong.
Having
said that, however, cosmic scientific research currently uses ever more AI
techniques, such as the aforementioned detection and discovery of gravitational
waves.
The
biggest challenge in capturing gravitational waves is that the sampling rate of
LIGO (Laser Interferometer Gravitational-Wave Observatory) data is extremely
high, reaching a frequency higher than 16,000 times per second, with tens of
thousands of sampling channels. Hence the amount of data is extremely large. It
is then understood that with AI machine learning, etc. and state-of-the-art methods
in the field of data processing, research efficiency can be improved. (1.
continued)
***Giancarlo
Elia Valori: Advisory Board Co-chair Honoris Causa Professor Giancarlo Elia
Valori is an eminent Italian economist and businessman. He holds prestigious
academic distinctions and national orders. Mr. Valori has lectured on
international affairs and economics at the world’s leading universities such as
Peking University, the Hebrew University of Jerusalem and the Yeshiva
University in New York. He currently chairs “International World Group”, he is
also the honorary president of Huawei Italy, economic adviser to the Chinese
giant HNA Group. In 1992 he was appointed Officier de la Légion d’Honneur de la
République Francaise, with this motivation: “A man who can see across borders
to understand the world” and in 2002 he received the title “Honorable” of the
Académie des Sciences de l’Institut de France. “
https://moderndiplomacy.eu/2023/09/28/artificial-intelligence-and-advances-in-physics-in-the-field-of-gravitational-waves-i/