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.
Recent advances in computational protein design have depended mainly on neural networks and machine learning to generate binders. However, the complexity of protein–protein interactions and the limitations of data-driven ...
Biotechnology
Jul 30, 2025
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Researchers from the University of Tokyo in collaboration with Aisin Corporation have demonstrated that universal scaling laws, which describe how the properties of a system change with size and scale, apply to deep neural ...
General Physics
Jul 18, 2025
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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 ...
Optics & Photonics
Jul 9, 2025
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Increasingly powerful AI models can make short-term weather forecasts with surprising accuracy. But neural networks only predict based on patterns from the past—what happens when the weather does something that's unprecedented ...
Earth Sciences
May 22, 2025
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Researchers at National Taiwan University have developed a new type of spintronic device that mimics how synapses work in the brain—offering a path to more energy-efficient and accurate artificial intelligence systems.
Condensed Matter
May 14, 2025
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22
Researchers from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences have developed a neural network model based on self-attention mechanisms to rapidly predict radiation shielding designs for space ...
Space Exploration
Apr 22, 2025
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24
The use of artificial intelligence (AI) scares many people as neural networks, modeled after the human brain, are so complex that even experts do not understand them. However, the risk to society of applying opaque algorithms ...
Astronomy
Mar 17, 2025
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174
International Iberian Nanotechnology Laboratory (INL) researchers have developed a neuromorphic photonic semiconductor neuron capable of processing optical information through self-sustained oscillations. Exploring the use ...
AI transformational impact is well under way. But as AI technologies develop, so too does their power consumption. Further advancements will require AI chips that can process AI calculations with high energy efficiency. This ...
Condensed Matter
Feb 6, 2025
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34
Super-resolution (SR) technology plays a pivotal role in enhancing the quality of images. SR reconstruction aims to generate high-resolution images from low-resolution ones. Traditional methods often result in blurred or ...
Optics & Photonics
Dec 30, 2024
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