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Category: mathematics

Multisynapse optical network outperforms digital AI models

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light instead of electricity to process information—promise faster speeds and lower energy use than traditional electronics.

But despite their potential, these systems have struggled to match the accuracy of digital . A key reason: most photonic systems still mimic the structure and training methods of digital models, introducing errors when translating from software to hardware.

Now, a research team from Northwestern Polytechnical University and Southeast University in China has developed a new kind of photonic neural network that breaks free from this digital imitation. Their design, published in Advanced Photonics Nexus, uses physical transformations of light to process information directly, without relying on mathematical models. This approach not only improves accuracy but also highlights a new direction for building smarter, faster AI hardware.

New particle acceleration strategy uses cold atoms to unlock cosmic mysteries

Scientists have used ultracold atoms to successfully demonstrate a novel method of particle acceleration that could unlock a new understanding of how cosmic rays behave, a new study reveals.

More than 70 years after its formulation, researchers have observed the Fermi acceleration mechanism in a laboratory by colliding against engineered movable potential barriers—delivering a significant milestone in high-energy astrophysics and beyond.

Fermi acceleration is the mechanism responsible for the generation of cosmic rays, as postulated by physicist Enrico Fermi in 1949. The process itself also features some universal properties that have spawned a wide range of mathematical models, such as the Fermi-Ulam model. Until now, however, it has been difficult to create a reliable Fermi accelerator on Earth.

Data Science and Machine Learning: Mathematical and

D.P. Kroese, Z.I. Botev, T. Taimre, R. Vaisman. Data Science and Machine Learning: Mathematical and Statistical Methods, Chapman and Hall/CRC, Boca Raton, 2019.

The purpose of this book is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.

The high-tech wizardry of integrated photonics

Inspired by the “Harry Potter” stories and the Disney Channel show “Wizards of Waverly Place,” 7-year-old Sabrina Corsetti emphatically declared to her parents one afternoon that she was, in fact, a wizard.

“My dad turned to me and said that, if I really wanted to be a wizard, then I should become a physicist. Physicists are the real wizards of the world,” she recalls.

That conversation stuck with Corsetti throughout her childhood, all the way up to her decision to double-major in physics and math in college, which set her on a path to MIT, where she is now a graduate student in the Department of Electrical Engineering and Computer Science.

While her work may not involve incantations or magic wands, Corsetti’s research centers on an area that often produces astonishing results: integrated photonics. A relatively young field, integrated photonics involves building computer chips that route light instead of electricity, enabling compact and scalable solutions for applications ranging from communications to sensing.

MIT graduate student Sabrina Corsetti is exploring the cutting edge of integrated photonics, which involves building computer chips that route light instead of electricity. Her projects have included a chip-sized 3D printer and miniaturized optical systems for quantum computing.

Machine learning outpaces supercomputers for simulating galaxy evolution coupled with supernova explosion

Researchers have used machine learning to dramatically speed up the processing time when simulating galaxy evolution coupled with supernova explosion. This approach could help us understand the origins of our own galaxy, particularly the elements essential for life in the Milky Way.

The findings are published in The Astrophysical Journal.

The team was led by Keiya Hirashima at the RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS) in Japan, along with colleagues from the Max Planck Institute for Astrophysics (MPA) and the Flatiron Institute.

Degrees of intelligence: Where Meta’s top AGI scientists studied and why it matters

Meta’s Superintelligence Lab has assembled a world-class team of AI researchers from institutions like OpenAI, DeepMind, and Google. Their educational paths—often beginning in top universities in China or India and leading to elite Western institutions—reflect the global and interdisciplinary nature of AGI development. This article explores their academic journeys, highlighting how rigorous training in mathematics, computer science, and safety research underpins the next frontier of artificial intelligence.

Faster topology optimization: An emerging industrial design technique gets a speed boost

With the rise of 3D printing and other advanced manufacturing methods, engineers can now build structures that were once impossible to fabricate. An emerging design strategy that takes full advantage of these new capabilities is topology optimization—a computer-driven technique that determines the most effective way to distribute material, leading to an optimized design.

Now, a research team including mathematicians from Brown University has developed a new approach that dramatically improves the speed and stability of topology optimization algorithms. The team, a collaboration between researchers at Brown, Lawrence Livermore National Laboratory and Simula Research Laboratory in Norway, detailed their work in two recently published papers in the SIAM Journal on Optimization and Structural and Multidisciplinary Optimization.

“Our method beats some existing methods by four or five times in terms of efficiency,” said Brendan Keith, an assistant professor of applied mathematics at Brown. “That’s a huge computational savings that could enable people to make designs more quickly and inexpensively, or to develop more complex designs with higher resolution.”

Satyendra Nath Bose

Satyendra Nath Bose FRS, MP [ 1 ] (/ ˈ b oʊ s / ; [ 4 ] [ a ] 1 January 1894 – 4 February 1974) was an Indian theoretical physicist and mathematician. He is best known for his work on quantum mechanics in the early 1920s, in developing the foundation for Bose–Einstein statistics, and the theory of the Bose–Einstein condensate. A Fellow of the Royal Society, he was awarded India’s second highest civilian award, the Padma Vibhushan, in 1954 by the Government of India. [ 5 ] [ 6 ] [ 7 ]

The eponymous particles class described by Bose’s statistics, bosons, were named by Paul Dirac. [ 8 ] [ 9 ]

A polymath, he had a wide range of interests in varied fields, including physics, mathematics, chemistry, biology, mineralogy, philosophy, arts, literature, and music. He served on many research and development committees in India, after independence. [ 10 ] .