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Design Features of Optical Diffraction Neural Networks.

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Title: Design Features of Optical Diffraction Neural Networks.
Authors: Konovalova, A. V.1 (AUTHOR), Popkova, A. A.1 (AUTHOR), Baluian, T. G.1 (AUTHOR), Fedyanin, A. A.1 (AUTHOR) fedyanin@nanolab.phys.msu.ru
Source: JETP Letters. Jan2026, Vol. 123 Issue 2, p85-92. 8p.
Subjects: Analog computers, Artificial neural networks, Computer simulation, Simulation methods & models, Systems design
Abstract: Recently, significant attention has been focused on finding and implementing approaches that would increase the efficiency of existing computational methods or create fundamentally new ones. One promising direction is the transition from digital to analog computing schemes, which allow for the design of high-performance specialized architectures based on known physical principles. In particular, a physical system in which a structure analogous to an artificial neural network can be implemented is a diffractive neural network. However, transferring computations to an analog platform entails the necessity of precise selection of a mathematical model that adequately accounts for the features of the physical implementation. In this work, the correctness of numerical modeling of a Fourier-diffractive neural network is experimentally tested, and the influence of the system configuration on the accuracy of the final computational result is numerically studied. [ABSTRACT FROM AUTHOR]
Copyright of JETP Letters is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="JN" term="%22JETP+Letters%22">JETP Letters</searchLink>. Jan2026, Vol. 123 Issue 2, p85-92. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Analog+computers%22">Analog computers</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+design%22">Systems design</searchLink>
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  Data: Recently, significant attention has been focused on finding and implementing approaches that would increase the efficiency of existing computational methods or create fundamentally new ones. One promising direction is the transition from digital to analog computing schemes, which allow for the design of high-performance specialized architectures based on known physical principles. In particular, a physical system in which a structure analogous to an artificial neural network can be implemented is a diffractive neural network. However, transferring computations to an analog platform entails the necessity of precise selection of a mathematical model that adequately accounts for the features of the physical implementation. In this work, the correctness of numerical modeling of a Fourier-diffractive neural network is experimentally tested, and the influence of the system configuration on the accuracy of the final computational result is numerically studied. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of JETP Letters is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jan2026
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