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Uncovering the nature of emergent magnetic monopoles

Scientists have discovered unique periodic structures in manganese germanide that behave like magnetic monopoles and antimonopoles. The researchers studied the collective excitation modes of these structures, revealing a way to experimentally determine their spatial configuration.

SourceWaseda University·JournalPhysical Review Letters·TypeComputational simulation/modeling·DateJun 12, 2024

Trapped in the middle: billiards with memory

Researchers from the University of Amsterdam have introduced a billiards game with memory, where the ball may never cross its own previous path. This leads to a trapping effect, making the system chaotic and fascinating, with many open mathematical questions and potential applications in physics and biophysics.

SourceUniversiteit van Amsterdam·JournalPhysical Review Letters·TypeData/statistical analysis·DateApr 11, 2024

Riddle of the sphinx

Researchers used the sphinx tile to explore geometry and chirality in life, finding unexpected properties related to its chirality. The study reveals superexponential increases in possible layouts as the number of sphinxes grows, with some tilings having nearly 72,000 possibilities.

SourceChan Zuckerberg Biohub·JournalPhysical Review Research·DateMar 21, 2024

How wind turbines react to turbulence

Researchers from the University of Oldenburg developed a new stochastic method to mitigate sudden swings in wind turbine power output. The study found that control systems are mainly responsible for short-term fluctuations and can be optimized to ensure more consistent energy output.

SourceUniversity of Oldenburg·JournalPRX Energy·TypeData/statistical analysis·DateSep 19, 2023

Machine learning models can produce reliable results even with limited training data

Researchers from University of Cambridge and Cornell University have developed a method to build machine learning models that can understand complex equations using far less training data. This breakthrough enables the construction of more time- and cost-efficient models for physics, engineering, and climate modeling applications.

SourceUniversity of Cambridge·JournalProceedings of the National Academy of Sciences·DateSep 19, 2023

The Ising on the cake

A team of researchers from Kyoto University and international institutions has developed a mathematical solution to the temporal asymmetry of nonequilibrium disordered Ising networks. This breakthrough offers insights into the behavior of biological systems, machine learning, and AI tools.

SourceKyoto University·JournalNature Communications·TypeComputational simulation/modeling·DateJul 4, 2023

Helium nuclei research advances our understanding of cosmic ray origin and propagation

The CALET team, including researchers from Waseda University, found that cosmic ray helium particles follow a Double Broken Power Law, indicating spectral hardening and softening in high-energy ranges. This deviation from expected power-law distribution suggests unique sources or mechanisms accelerating and propagating helium nuclei.

SourceWaseda University·JournalPhysical Review Letters·TypeObservational study·DateMay 25, 2023

Scurrying centipedes inspire many-legged robots that can traverse difficult landscapes

Researchers developed a new theory of multilegged locomotion, creating robotic models that can move across uneven surfaces without sensors. The robot's leg redundancy enables it to transport itself and loads on challenging terrain, making it suitable for applications like agriculture, space exploration, and search and rescue.

SourceGeorgia Institute of Technology·JournalScience·TypeExperimental study·DateMay 4, 2023

Scientific AI’s ‘black box’ is no match for 200-year-old method

A new study uses Fourier analysis to understand how deep neural networks learn complex physics. By analyzing the equation of a fully trained model, researchers were able to identify crucial information about how the network learns and generalizes. This breakthrough could accelerate the use of scientific deep learning in climate science.

SourceRice University·JournalPNAS Nexus·TypeComputational simulation/modeling·DateFeb 13, 2023

Waseda University researchers measure boron flux in high-energy cosmic rays with the CALorimetric Electron Telescope (CALET)

Researchers from Waseda University measured the energy spectrum of boron and the B/C flux ratio in high-energy cosmic rays using the CALorimetric Electron Telescope. The results indicate a different spectral index for boron compared to carbon, with implications for our understanding of cosmic ray propagation mechanisms.

SourceWaseda University·JournalPhysical Review Letters·TypeObservational study·DateJan 26, 2023

A closer look at the dynamics of the p-Laplacian Allen–Cahn equation

A team of researchers from Korea investigated the dynamics of the p-Laplacian AC equation, finding that solutions maintain three criteria: phase separation, boundedness, and energy decay properties. They also identified an advantage of p-AC equation over classical Laplacian in adjusting interface sharpness.

SourceIncheon National University·JournalApplied Mathematics and Computation·TypeExperimental study·DateNov 21, 2022

New research tunes theory of sound levitation

Researchers at the University of Technology Sydney have extended the theory of acoustic levitation to account for asymmetrical particles, which is more applicable to real-world experience. This new understanding enables precise control and sorting of tiny objects using ultrasonic waves.

SourceUniversity of Technology Sydney·JournalPhysical Review Letters·TypeComputational simulation/modeling·DateOct 18, 2022

Artificial intelligence reduces a 100,000-equation quantum physics problem to only four equations

Physicists used machine learning to compress a complex quantum problem into four equations, capturing the physics of electrons on a lattice with high accuracy. The approach could revolutionize how scientists investigate systems containing many interacting electrons and potentially aid in designing materials with sought-after properties.

SourceSimons Foundation·JournalPhysical Review Letters·TypeComputational simulation/modeling·DateSep 26, 2022

New, highly tunable composite materials—with a twist

Researchers at the University of Utah designed composite materials using moiré patterns, resulting in abrupt transitions between electrical conductor and insulator properties. The study's findings have broad potential technological applications and demonstrate a new geometry-driven localization transition.

SourceUniversity of Utah·JournalCommunications Physics·TypeComputational simulation/modeling·DateJun 14, 2022

When more complex is simpler: ‘Coarse-graining’ can help scientists understand complex microbial ecosystems

A new modeling framework suggests that some microbial ecosystems are more easily understood through coarse-graining, which involves omitting details. This approach could help biologists study microbes in their natural environments, rather than isolating them in a petri dish.

SourceWashington University in St. Louis·JournalPhysical Review X·TypeComputational simulation/modeling·DateMay 16, 2022

A ‘cautionary tale’ about location tracking

A recent study by the University of Rochester found that mobility patterns can be predicted with surprising accuracy based on data collected from acquaintances, even if individual users turn off their own location tracking. The researchers discovered that up to 95% of an individual's movement pattern can be inferred from people they ar...

SourceUniversity of Rochester·JournalNature Communications·TypeData/statistical analysis·DateApr 12, 2022

Rational neural network advances machine-human discovery

A novel 'rational' neural network reveals underlying mathematical equations through Green's functions, enabling humans to understand machine-generated findings. This breakthrough in partial differential equation learning holds promise for advancing scientific exploration of weather systems, climate change, and more.

SourceCornell University·JournalScientific Reports·DateApr 5, 2022

Quantum information theory: Quantum complexity grows linearly for an exponentially long time

Researchers proved a conjecture on quantum complexity growth, contradicting the Brown-Susskind intuition that complexity increases linearly for astronomically long times and then remains maximum. Instead, complexity grows linearly with time until it saturates at an exponential point related to system size.

SourceHelmholtz-Zentrum Berlin für Materialien und Energie·JournalNature Physics·TypeComputational simulation/modeling·DateMar 28, 2022