Artificial neural networks, as physical systems, are implemented through hardware substrates that realize interconnected processing units and weighted connections using electronic, optical, or neuromorphic architectures. In conventional digital hardware, they are instantiated as configurations of logic gates, memory arrays, and interconnects on CPUs, GPUs, or specialized accelerators (e.g., TPUs), where weights reside in volatile or non-volatile memory and computation is executed via parallel multiply-accumulate operations. Emerging neuromorphic and analog implementations encode synaptic weights in device conductances (e.g., memristors, phase-change materials) and exploit device physics for in-memory computation, enabling high spatiotemporal parallelism, low-latency signal propagation, and energy-efficient approximation of neural operations in a physically embedded network topology.
Hybrid climate modeling has emerged as an effective way to reduce the computational costs associated with cloud-resolving models while retaining their accuracy. The approach retains physics-based models to simulate large-scale ...
Earth Sciences
Feb 7, 2026
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Scientists at the U.S. Department of Energy's (DOE) Brookhaven National Laboratory have developed a novel artificial intelligence (AI)-based method to dramatically tame the flood of data generated by particle detectors at ...
General Physics
Feb 2, 2026
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28
Recent advances in the field of artificial intelligence (AI) have opened new exciting possibilities for the rapid analysis of data, the sourcing of information and the generation of use-specific content. To run AI models, ...
A research team from the Institute of Modern Physics of the Chinese Academy of Sciences and Lanzhou University has obtained important experimental evidence for revealing brain memory mechanisms and developing a new type of ...
Bio & Medicine
Jan 7, 2026
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23
Neuroscientists have been trying to understand how the brain processes visual information for over a century. The development of computational models inspired by the brain's layered organization, also known as deep neural ...
AI has successfully been applied in many areas of science, advancing technologies like weather prediction and protein folding. However, there have been limitations for the world of scientific discovery involving more curiosity-driven ...
A research team from the Xinjiang Astronomical Observatory (XAO) of the Chinese Academy of Sciences has developed an interpretable artificial intelligence (AI) framework named Convolutional Kolmogorov–Arnold Network (CKAN), ...
Astronomy
Dec 18, 2025
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38
A new algorithmic framework that can predict flooding could help save lives and reduce the devastation as climate change drives more intense and unpredictable rainfall.
Earth Sciences
Dec 1, 2025
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16
Past psychology and behavioral science studies have identified various ways in which people's acquisition of new knowledge can be disrupted. One of these, known as interference, occurs when humans are learning new information ...
Machine learning models called convolutional neural networks (CNNs) power technologies like image recognition and language translation. A quantum counterpart—known as a quantum convolutional neural network (QCNN)—could process ...
Optics & Photonics
Nov 25, 2025
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