While text-to-video artificial intelligence models like OpenAI’s Sora are rapidly metamorphosing in front of our eyes, they have struggled to produce metamorphic videos. Simulating a tree sprouting or a flower blooming is harder for AI systems than generating other types of videos because it requires the knowledge of the physical world and can vary widely.

But now, these models have taken an evolutionary step.

Computer scientists at the University of Rochester, Peking University, University of California, Santa Cruz, and National University of Singapore developed a new AI text-to-video model that learns real-world physics knowledge from time-lapse videos. The team outlines their model, MagicTime, in a paper published in IEEE Transactions on Pattern Analysis and Machine Intelligence.

The mechanism that stabilizes new ferroelectric semiconductors also creates a conductive pathway, which could make them suitable for use in high-power transistors. A new type of semiconductor that can store information using electric fields may lead to more energy-efficient computers, ultra-preci

When an object moves extremely fast—close to the speed of light—certain basic assumptions that we take for granted no longer apply. This is the central consequence of Albert Einstein’s special theory of relativity. The object then has a different length than when it is at rest, and time passes differently for the object than it does in the laboratory. All this has been repeatedly confirmed in experiments.

However, one interesting consequence of relativity has not yet been observed—the so-called Terrell-Penrose effect. In 1959, physicists James Terrell and Roger Penrose (Nobel laureate in 2020) independently concluded that fast-moving objects should appear rotated. However, this effect has never been demonstrated.

Now, a collaboration between TU Wien (Vienna) and the University of Vienna has succeeded for the first time in reproducing the effect using laser pulses and precision cameras—at an effective speed of light of 2 meters per second. The research is published in the journal Communications Physics.

It inspired further work — mathematicians like Sophie Germain had previously contributed techniques (notably the “Sophie Germain trick” for special primes), and Dirichlet’s work continued the trend of applying novel number-theoretic tools.

(/ ˌ d ɪər ɪ ˈ k l eɪ / ; ^{[ 1 ]} German: [ləˈʒœn diʁiˈkleː] ; ^{[ 2 ]} 13 February 1805 – 5 May 1859) was a German mathematician. In number theory, he proved special cases of Fermat’s last theorem and created analytic number theory. In analysis, he advanced the theory of Fourier series and was one of the first to give the modern formal definition of a function. In mathematical physics, he studied potential theory, boundary-value problems, and heat diffusion, and hydrodynamics.

Although his surname is Lejeune Dirichlet, he is commonly referred to by his mononym Dirichlet, in particular for results named after him.

In 2024 a shockwave rippled through the astronomical world, shaking it to the core. The disturbance didn’t come from some astral disaster at the solar system’s doorstep, however. Rather it arrived via the careful analysis of many far-distant galaxies, which revealed new details of the universe’s evolution across eons of cosmic history. Against most experts’ expectations, the result suggested that dark energy —the mysterious force driving the universe’s accelerating expansion—was not an unwavering constant but rather a more fickle beast that was weakening over time.

The shocking claim’s source was the Dark Energy Spectroscopic Instrument (DESI), run by an international collaboration at Kitt Peak National Observatory in Arizona. And it was so surprising because cosmologists’ best explanations for the universe’s observed large-scale structure have long assumed that dark energy is a simple, steady thing. But as Joshua Frieman, a physicist at the University of Chicago, says: “We tend to stick with the simplest theory that works—until it doesn’t.” Heady with delight and confusion, theorists began scrambling to explain DESI’s findings and resurfaced old, more complex ideas shelved decades ago.

In March 2025 even more evidence accrued in favor of dark energy’s dynamic nature in DESI’s latest data release—this time from a much larger, multimillion-galaxy sample. Dark energy’s implied fading, it seemed, was refusing to fade away.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a novel artificial intelligence (AI) model inspired by neural oscillations in the brain, with the goal of significantly advancing how machine learning algorithms handle long sequences of data.

AI often struggles with analyzing complex information that unfolds over long periods of time, such as climate trends, biological signals, or financial data. One new type of AI model called “state-space models” has been designed specifically to understand these sequential patterns more effectively. However, existing state-space models often face challenges—they can become unstable or require a significant amount of computational resources when processing long data sequences.

To address these issues, CSAIL researchers T. Konstantin Rusch and Daniela Rus have developed what they call “linear oscillatory state-space models” (LinOSS), which leverage principles of forced harmonic oscillators—a concept deeply rooted in physics and observed in biological neural networks.

Four physicists at the Hebrew University of Jerusalem, in Israel, have unraveled the mechanical process behind the growth of roses as they blossom into their unique shape. In their study published in the journal Science, Yafei Zhang, Omri Cohen, Michael Moshe and Eran Sharon adopted a multipronged approach to learn the secrets behind rose blossom growth. Qinghao Cui and Lishuai Jin, with the University of Hong Kong have published a Perspective piece in the same journal issue outlining the work.

Roses have been prized for their beauty and sweet aromas for thousands of years, but until now, the mechanics behind rose growth have not been explored. To gain a better understanding of the process, the research team undertook a three-pronged approach. First, they conducted a theoretical analysis of the process. Then they created computer models to simulate the ways the flowers might grow and bloom; finally, they created real-world bendable plastic disks to simulate petals and the possible ways they could grow given the constraints of real roses.

They found that the shape of the petals is strongly influenced by the frustration known as the Mainardi-Codazzi-Peterson incompatibility, in which geometric compatibility conditions inherent on a surface made of a particular material are violated, leading to forces that generate rolling and sharp edges.

Researchers have created the first laboratory analog of the ‘black hole bomb’, a theoretical concept developed by physicists in the 1970s.

Using high-density electrophysiological recordings, how internally generated cell assemblies are updated by action plans to meet external goals is explored.

A breakthrough in Hilbert’s sixth problem is a major step in grounding physics in math