A neural network discovered Copernicus’ heliocentricity on its own

Can neural networks help scientists discover laws about more complex phenomena, like quantum mechanics?

A neural network discovered Copernicus’ heliocentricity on its own
  • Scientists trained a neural network to predict the movements of Mars and the Sun.
  • In the process, the network generated formulae that place the Sun at the center of our solar system.
  • The case suggests that machine-learning techniques could help reveal new laws of physics.


A neural network was able to rediscover one of the most important paradigm shifts in scientific history: Earth and other planets revolve around the Sun. The accomplishment suggests machine-learning techniques could someday help to reveal new laws of physics, maybe even within the complex realm of quantum mechanics.

The results are set to appear in the journal Physical Review Letters, according to Nature.

The neural network — a machine-learning algorithm called SciNetwas shown measurements of how the Sun and Mars appear from Earth against the fixed-star background of the night sky. SciNet's task, assigned by a team of scientists at the Swiss Federal Institute of Technology, was to predict where the Sun and Mars would be at future points in time.

Copernicus-style formulae

In the process, SciNet generated formulas that place the Sun at the center of our solar system. Remarkably, SciNet accomplished this in a way similar to how astronomer Nicolaus Copernicus discovered heliocentricity.

"In the 16th century, Copernicus measured the angles between a distant fixed star and several planets and celestial bodies and hypothesized that the Sun, and not the Earth, is in the centre of our solar system and that the planets move around the Sun on simple orbits," the team wrote in a paper published on the preprint repository arXiv. "This explains the complicated orbits as seen from Earth."

The team "encouraged" SciNet to come up with ways to predict the movements of the Sun and Mars in the simplest way possible. To do that, SciNet passes information back and forth between two sub-networks. One network "learns" from data, and the other uses that knowledge to make predictions and test their accuracy. These networks are connected to each other by only a few links, so when they communicate, information is compressed, resulting in "simpler" representations.

Renner et al.

SciNet decided that the simplest way to predict the movements of celestial bodies was through a model that places the Sun at the center of our solar system. So, the neural network didn't necessarily "discover" heliocentricity, but rather described it through mathematics that humans can interpret.

Building humanlike AI

In 2017, data scientist Brenden Lake and his colleagues wrote a paper describing what it will take to build machines that learn and think like people. One benchmark for doing so would be artificial intelligence that can describe the physical world. At the time, they said it "remains to be seen" whether "deep networks trained on physics-related data" could discover laws of physics on their own. In a narrow sense, SciNet passes this test.

"To summarize, the main aim of this work is to show that neural networks can be used to discover physical concepts without any prior knowledge," the SciNet team wrote. "To achieve this goal, we introduced a neural network architecture that models the physical reasoning process. The examples illustrate that this architecture allows us to extract physically relevant data from experiments, without imposing further knowledge about physics or mathematics."

How New York's largest hospital system is predicting COVID-19 spikes

Northwell Health is using insights from website traffic to forecast COVID-19 hospitalizations two weeks in the future.

Credit: Getty Images
Sponsored by Northwell Health
  • The machine-learning algorithm works by analyzing the online behavior of visitors to the Northwell Health website and comparing that data to future COVID-19 hospitalizations.
  • The tool, which uses anonymized data, has so far predicted hospitalizations with an accuracy rate of 80 percent.
  • Machine-learning tools are helping health-care professionals worldwide better constrain and treat COVID-19.
Keep reading Show less

Listen: Scientists re-create voice of 3,000-year-old Egyptian mummy

Scientists used CT scanning and 3D-printing technology to re-create the voice of Nesyamun, an ancient Egyptian priest.

Surprising Science
  • Scientists printed a 3D replica of the vocal tract of Nesyamun, an Egyptian priest whose mummified corpse has been on display in the UK for two centuries.
  • With the help of an electronic device, the reproduced voice is able to "speak" a vowel noise.
  • The team behind the "Voices of the Past" project suggest reproducing ancient voices could make museum experiences more dynamic.
Keep reading Show less

Put on a happy face? “Deep acting” associated with improved work life

New research suggests you can't fake your emotional state to improve your work life — you have to feel it.

Credit: Columbia Pictures
Personal Growth
  • Deep acting is the work strategy of regulating your emotions to match a desired state.
  • New research suggests that deep acting reduces fatigue, improves trust, and advances goal progress over other regulation strategies.
  • Further research suggests learning to attune our emotions for deep acting is a beneficial work-life strategy.
  • Keep reading Show less

    World's oldest work of art found in a hidden Indonesian valley

    Archaeologists discover a cave painting of a wild pig that is now the world's oldest dated work of representational art.

    Pig painting at Leang Tedongnge in Indonesia, made at 45,500 years ago.

    Credit: Maxime Aubert
    Surprising Science
    • Archaeologists find a cave painting of a wild pig that is at least 45,500 years old.
    • The painting is the earliest known work of representational art.
    • The discovery was made in a remote valley on the Indonesian island of Sulawesi.
    Keep reading Show less
    Mind & Brain

    What can Avicenna teach us about the mind-body problem?

    The Persian polymath and philosopher of the Islamic Golden Age teaches us about self-awareness.

    Scroll down to load more…
    Quantcast