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Category: information science – Page 3

https://lnkd.in/gUDFq8KF Explicit solution of Navier Stokes Equation A millennium problem! can we prove that fluid motion always stays smooth, or can it blow up into chaos?

Here’s the equation that rules all fluids: ρ (∂u/∂t + (u·∇)u) = −∇p + μ∇²u + f What it means: — u: velocity field (how the fluid moves) — p: pressure — μ: viscosity (internal friction) — ρ: density — f: external forces (like gravity) Instead of solving the velocity u directly, he treats the fluid like a symphony of interacting notes: φ(x, t) = ∫ d³k [ aₖ e^(-iωt + ik·x) + aₖ† e^(iωt — ik·x) ] Each aₖ and aₖ† represent creation and annihilation operators — the conductors of the quantum orchestra of sound. 🎵 Analogy: Fluid as a Symphony Imagine a calm pond. Every ripple is a gentle musical note. Now drop many stones — the ripples overlap, collide, and amplify. That’s turbulence.

AI tools fall short in predicting suicide, study finds

The accuracy of machine learning algorithms for predicting suicidal behavior is too low to be useful for screening or for prioritizing high-risk individuals for interventions, according to a new study published September 11 in the open-access journal PLOS Medicine by Matthew Spittal of the University of Melbourne, Australia, and colleagues.

Numerous risk assessment scales have been developed over the past 50 years to identify patients at high risk of suicide or self-harm. In general, these scales have had poor predictive accuracy, but the availability of modern machine learning methods combined with electronic health record data has re-focused attention on developing to predict suicide and self-harm.

In the new study, researchers undertook a systemic review and meta-analysis of 53 previous studies that used machine learning algorithms to predict suicide, self-harm and a combined suicide/self-harm outcome. In all, the studies involved more than 35 million and nearly 250,000 cases of suicide or hospital-treated self-harm.

Statistical mechanics method helps machines better understand complex systems

A study by University of Hawaiʻi researchers is advancing how we learn the laws that govern complex systems—from predator-prey relationships to traffic patterns in cities to how populations grow and shift—using artificial intelligence (AI) and physics.

The research, published in Physical Review Research, introduces a new method based on to improve the discovery of equations directly from noisy real-world data. Statistical mechanics is a branch of physics that explains how collective behavior emerges from individual particles, such as how the random motion of gas molecules leads to predictable changes in pressure and temperature.

In this new work, statistical mechanics is used to understand how different mathematical models “compete” when trying to explain a system. This matters because many scientific fields rely on understanding how systems change over time, whether tracking disease spread, analyzing or predicting the stock market. But real-world data is often messy, and traditional AI models can be unreliable when the data gets noisy or incomplete.

Algorithm maps genetic connection between Alzheimer’s and specific neurons

The number of people living with dementia worldwide was estimated at 57 million in 2021 with nearly 10 million new cases recorded each year. In the U.S., dementia impacts more than 6 million lives, and the number of new cases is expected to double over the next few decades, according to a 2025 study. Despite advancements in the field, a full understanding of disease-causing mechanisms is still lacking.

To address this gap, Rice University researchers and collaborators at Boston University have developed a that can help identify which specific types of cells in the body are genetically linked to complex human traits and diseases, including in forms of dementia such as Alzheimer’s and Parkinson’s.

Known as “Single-cell Expression Integration System for Mapping genetically implicated Cell types,” or seismic, the tool helped the team hone in on genetic vulnerabilities in memory-making brain cells that link them to Alzheimer’s ⎯ the first to establish an association based on a genetic link between the disease and these specific neurons. The algorithm outperforms existing tools for identifying that are potentially relevant in complex diseases and is applicable in disease contexts beyond dementia.

Quantum Systems Modeled Without Prior Assumptions

An improved algorithm for learning the static and dynamic properties of a quantum system could have applications in quantum computing, simulation, and sensing.

Quantum systems are notoriously hard to study, control, and simulate. One key reason is that their full characterization requires a vast amount of information. Fortunately, in the past decade, scientists have shown that many physical properties of a quantum system can be efficiently predicted using much less information [1, 2]. Moreover, researchers have built quantum sensors that can measure these properties with a much smaller uncertainty compared with the best classical sensors [3]. Nevertheless, it has been difficult to achieve both efficient predictions and precise measurements at the same time. Now, building on previous breakthroughs in the field, Hong-Ye Hu at Harvard University and his colleagues have demonstrated a new algorithm that characterizes quantum systems of any size with optimal efficiency and precision [4].

Quantum simulations that once needed supercomputers now run on laptops

UB physicists have upgraded an old quantum shortcut, allowing ordinary laptops to solve problems that once needed supercomputers. A team at the University at Buffalo has made it possible to simulate complex quantum systems without needing a supercomputer. By expanding the truncated Wigner approximation, they’ve created an accessible, efficient way to model real-world quantum behavior. Their method translates dense equations into a ready-to-use format that runs on ordinary computers. It could transform how physicists explore quantum phenomena.

Picture diving deep into the quantum realm, where unimaginably small particles can exist and interact in more than a trillion possible ways at the same time.

It’s as complex as it sounds. To understand these mind-bending systems and their countless configurations, physicists usually turn to powerful supercomputers or artificial intelligence for help.

Mathematical model reveals why cracks sharpen during rapid rubber fracture

A research group from the University of Osaka, Zen University, and the University of Tokyo has mathematically uncovered the mechanism that causes crack tips to sharpen during the rapid fracture of rubber.

The bursting of balloons or tire blowouts is caused by rapid fracture, a phenomenon in which a small crack propagates instantaneously. During this process, the crack tip sharpens, accelerating the fracture. However, the reason behind this sharpening had long remained unexplained. Traditionally, it was believed to result from the material’s complex nonlinear effects.

The research group—comprising Hokuto Nagatakiya, a doctoral student; Shunsuke Kobayashi, assistant professor; and Ryuichi Tarumi, professor at the University of Osaka; along with Naoyuki Sakumichi, associate professor at Zen University and project associate professor at the University of Tokyo—has mathematically solved the problem of crack propagation. They derived equations that describe both the shape of the crack and the overall deformation of the material.

New AI model for drug design brings more physics to bear in predictions

When machine learning is used to suggest new potential scientific insights or directions, algorithms sometimes offer solutions that are not physically sound.

Take, for example, AlphaFold, the AI system that predicts the complex ways in which amino acid chains will fold into 3D protein structures. The system sometimes suggests “unphysical” folds—configurations that are implausible based on the —especially when asked to predict the folds for chains that are significantly different from its .

To limit this type of unphysical result in the realm of drug design, Anima Anandkumar, Bren Professor of Computing and Mathematical Sciences at Caltech, and her colleagues have introduced a new machine learning model called NucleusDiff, which incorporates a simple physical idea into its training, greatly improving the algorithm’s performance.

Vortices in ultralight dark matter halos could reveal new clues to cosmic structure

The nature of dark matter remains one of the greatest mysteries in cosmology. Within the standard framework of non-collisional cold dark matter (CDM), various models are considered: WIMPs (Weakly Interacting Massive Particles, with masses of around 100 GeV/c2), primordial black holes, and ultralight axion-like particles (mass of 10-22 to 1 eV/c2). In the latter case, dark matter behaves like a wave, described by a Schrödinger equation, rather than as a collection of point particles. This generates specific behaviors at small scales, while following standard dynamics (CDM) at large scales.

Philippe Brax and Patrick Valageas, researchers at the Institute of Theoretical Physics, studied models of ultralight cold dark matter with repulsive self-interactions, whose dynamics are described by a non-linear variant of the Schrödinger equation, known as the Gross-Pitaevskii equation, also encountered in the physics of superfluids and Bose-Einstein condensates. In their work, the authors follow the formation and dynamics of particular structures, called “vortices” (whirlpools) and “solitons” (cores in hydrostatic equilibrium), within halos of rotating ultralight dark matter.

The papers are published in the journal Physical Review D.

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