Physicists debut a new method to detect gravitational waves with unprecedented precision, providing insights into black hole mergers.
A team of researchers from Peking University and the Eastern Institute of Technology (EIT) in China has developed a new framework to train machine learning models with prior knowledge, such as the laws of physics or mathematical logic, alongside data.
Chinese researchers are on the brink of pioneering a groundbreaking approach to developing ‘AI scientists capable of conducting experiments and solving scientific problems.
Recent advances in deep learning models have revolutionized scientific research, but current models still struggle to simulate real-world physics interactions accurately.
“Without a fundamental understanding of the world, a model is essentially an animation rather than a simulation,” said Chen Yuntian, study author and a professor at the Eastern Institute of Technology (EIT).
Deep learning models are generally trained using data and not prior knowledge, which can include things such as the laws of physics or mathematical logic, according to the paper.
But the scientists from Peking University and EIT wrote that when training the models, prior knowledge could be used alongside data to make them more accurate, creating “informed machine learning” models capable of incorporating this knowledge into their output.
The FIT4NANO project has mapped out the expansive applications and future directions of focused ion beam technology, emphasizing its critical role in advancing research and development across multiple disciplines, from microelectronics to life sciences.
Processing materials on the nanoscale, producing prototypes for microelectronics, or analyzing biological samples: The range of applications for finely focused ion beams is huge. Experts from the EU collaboration FIT4NANO have now reviewed the many options and developed a roadmap for the future. The article, published in Applied Physics Review, is aimed at students, users from industry and science as well as research policymakers.
Discovery and Applications.
A team of engineers, physicists, and data scientists from Princeton University and the Princeton Plasma Physics Laboratory (PPPL) have used artificial intelligence (AI) to predict—and then avoid—the formation of a specific type of plasma instability in magnetic confinement fusion tokamaks. The researchers built and trained a model using past experimental data from operations at the DIII-D National Fusion Facility in San Diego, Calif., before proving through real-time experiments that their model could forecast so-called tearing mode instabilities up to 300 milliseconds in advance—enough time for an AI controller to adjust operating parameters and avoid a tear in the plasma that could potentially end the fusion reaction.
Some excellent food for thought face_with_colon_three
We now have everything we need to build a physics engine with infinite precision.
Continue reading “Perfect, Infinite-Precision, Game Physics in Python (Part 3)” »
Dr Freese has also made the case for a Dark Big Bang that could have given rise to dark matter independently of normal matter in the days after the Big Bang. The traditional model of the universe says that matter and dark matter were produced at the same time. The earliest evidence of dark matter, however, only appears later in the early evolution of the universe, when cosmic structure starts to form.
One explanation for this is that matter and dark matter did not, in fact, appear together, but that dark matter entered the universe in a second cataclysmic release of energy from the vacuum—the Dark Big Bang—as much as a month after the traditional Big Bang. The model that Dr Freese and her co-author Martin Winkler explored would explain why dark matter might be completely decoupled from traditional matter and it also naturally produces SIDM candidates. If there was such a Dark Big Bang, it would have left a clear signature—a pattern in the frequencies of the gravitational waves that hum across the universe—that could be picked up by future gravitational-wave detectors.
One of the greatest problems in modern physics is to reconcile the enormous difference between the energy carried by random fluctuations in the vacuum of space, and the dark energy driving the universe’s expansion.
Through new research published in The European Physical Journal Plus, researchers led by Enrico Calloni at the University of Naples Federico II, Italy, have unveiled a prototype for an ultra-precise beam balance instrument, which they hope could be used to measure the interaction between these vacuum fluctuations and gravitational fields. With some further improvements, the instrument could eventually enable researchers to shed new light on the enigmatic origins of dark energy.
Inside a vacuum, electromagnetic waves are constantly emerging and disappearing through random fluctuations, so that even though the space doesn’t contain any matter, it still carries a certain amount of energy. Through their research, Calloni’s team aimed to measure the influence of these fluctuations using a state-of-the-art beam balance.
The thermal hall effect (THE) is a physical phenomenon characterized by tiny transverse temperature differences occurring in a material when a thermal current passes through it and a perpendicular magnetic field is applied to it. This effect has been observed in a growing number of insulators, yet its underlying physics remains poorly understood.
Researchers at Université de Sherbrooke in Canada have been trying to identify the mechanism behind this effect in different materials. Their most recent paper, published in Nature Physics, specifically examined this effect in the antiferromagnetic insulator strontium iridium oxide (Sr2IrO4).
“Our current research activity on the THE in insulators started with our discovery of a large THE in cuprate superconductors,” Louis Taillefer, co-author of the paper, told Phys.org.
Scientists have revealed why some white dwarfs mysteriously stop cooling—changing ideas on just how old stars really are and what happens to them when they die.
White dwarf stars are universally believed to be ‘dead stars’ that continuously cool down over time. However, in 2019, data from the European Space Agency’s (ESA’s) Gaia satellite discovered a population of white dwarf stars that have stopped cooling for more than eight billion years. This suggested that some white dwarfs can generate significant extra energy, at odds with the classical ‘dead star’ picture, and astronomers initially were not sure how this could happen.
Today, new research published in Nature, led by Dr. Antoine Bédard from the University of Warwick and Dr. Simon Blouin from the University of Victoria (Canada), unveils the mechanism behind this baffling observation.
