Articles by Ali Momeni
Creation of a computational space with model-free metasurface neural network
Abstract Physical neural networks (PNNs) are neural-like computational frameworks that exploit the intrinsic dynamics of physical media to achieve ultrafast and energy-efficient information processing. However, the complex and strongly-coupled physical nature of PNNs in disordered environments makes them resistant to accurate differentiable modeling. Here, we propose a concept of computational space that empowers the chaotic environment itself with computational capabilities.
[2601.09139] Dynamic Hierarchical $j$-Tree Decomposition and Its Applications
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Training of physical neural networks - Nature
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Abstract Physical neural networks (PNNs) are a class of neural-like networks that make use of analogue physical systems to perform computations. Although at present confined to small-scale laboratory demonstrations, PNNs could one day transform how artificial intelligence (AI) calculations are performed. Could we train AI models many orders of magnitude larger than present ones? Could we perform model inference locally and privately on edge devices?
By Ali Momeni, Babak Rahmani, Benjamin Scellier, Peter McMahon, Yuhang Li, Ilker Oguz, Francesco Morichetti, Philipp del Hougne, Manuel Le Gallo, Abu Sebastian, Azalia Mirhoseini, Danijela Marković, Daniel Brunner, Christophe Moser, Sylvain Gigan, Florian Marquardt, Aydogan Ozcan, Julie Grollier, Andrea Liu, Demetri Psaltis, Andrea Alu
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Nature
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Experimental evaluation of an environmentally friendly drilling fluid for clay stabilization in shale formations - Scientific Reports
Abstract To face the problems of shale drilling, oil-based fluids, mineral salts, and polymers are used, but they have limitations such as high preparation cost, hazardous environmental impact, and limited applicability in high-pressure, high-temperature environments. Therefore, introducing an environmentally friendly drilling fluid capable of inhibiting clay swelling can be beneficial for drilling shale layers and other layers containing swelling clays.
[2504.18161] Fully Dynamic Algorithms for Transitive Reduction
arXiv:2504.18161 (cs) View PDF HTML (experimental) Comments: Subjects: Data Structures and Algorithms (cs.DS) Cite as: arXiv:2504.18161 [cs.DS] (or arXiv:2504.18161v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2504.18161 Submission history From: Ali Momeni [ view email] [v1] Fri, 25 Apr 2025 08:20:44 UTC (52 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps...
Effect of orbital decompression surgery on the choroidal profile in patients with thyroid eye disease - Scientific Reports
Abstract This study aimed to investigate the effect of orbital wall decompression surgery and reduction of proptosis on the choroidal vascularity index (CVI) and subfoveal choroidal thickness (SFCT) in patients with thyroid eye disease (TED). Fifty-one eyes from 38 patients with controlled TED and proptosis were enrolled in this study. The majority of the patients (50.9%) had a clinical activity score (CAS) of zero, and none had a CAS greater than 2.
[2404.11389] Finding $d$-Cuts in Graphs of Bounded Diameter, Graphs of Bounded Radius and $H$-Free Graphs
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Backpropagation-free training of deep physical neural networks
Abstract Recent successes in deep learning for vision and natural language processing are attributed to larger models but come with energy consumption and scalability issues. Current training of digital deep learning models primarily relies on backpropagation that is unsuitable for physical implementation.
Backpropagation-free training of deep physical neural networks
Supplementary Materials Materials and Methods Supplementary Text Figs. S1 to S18 Tables S1 to S6 References (60–69) References and Notes 1 Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature521, 436–444 (2015). 2 L. Deng, D. Yu, Deep learning: Methods and applications. Found. Trends Signal Process.7, 197–387 (2014). 3 M. Asghari, A. V. Krishnamoorthy, Energy-efficient communication. Nat. Photonics5, 268–270 (2011). 4 D. Patterson, J. Gonzalez, Q. Le, C. Liang, L.-M. Munguia, D. Rothchild, D. So, M. Texier, J.
The Sensitivity of Micro—Macro Mechanical Behaviour of Sand to the Inter-Particle Properties
This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by Ali Momeni 1, Kenneth Imo-Imo Israel Eshiet 2 and Yong Sheng 3,* 1 WSP, 3 Wellington Pl, Leeds LS1 4AP, UK 2 Faculty of Science and Engineering, School of Architecture and the Built Environment, University of Wolverhampton, Wolverhampton WV1 1LY, UK 3 School of Computer Science, University of Hull, Hull HU6 7RX, UK * Author to whom correspondence should be addressed.
Publisher Correction: An Information Theory-Inspired Strategy for Design of Re-programmable Encrypted Graphene-based Coding Metasurfaces at Terahertz Frequencies
Publisher Correction: An Information Theory-Inspired Strategy for Design of Re-programmable Encrypted Graphene-based Coding Metasurfaces at Terahertz Frequencies Correction to: Scientific Reports https://doi.org/10.1038/s41598-018-24553-2, published online 18 April 2018 The Supplementary Information file that accompanies this Article contains technical errors, where mathematical symbols and equations are not displayed correctly.
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