作者：Lu, Hongfang; Cheng, Y. Frank
作者单位：Chinese Academy of Sciences; Ningbo Institute of Materials Technology & Engineering, CAS
作者：Reyes-Suarez, Juan C.; Buitrago, Manuel; Barros, Brais; Mammeri, Safae; Makoond, Nirvan; Lazaro, Carlos; Riveiro, Belen; Adam, Jose M.
作者单位：Universitat Politecnica de Valencia; Universidade de Vigo; Universitat Politecnica de Valencia
摘要：Steel truss bridges are constructed by connecting many different types of bars (components) to form a load-bearing structural system. Several disastrous collapses of this type of bridge have occurred as a result of initial component failure(s) propagating to the rest of the structure1, 2-3. Despite the prevalence and importance of these structures, it is still unclear why initial component failures propagate disproportionately in some bridges but barely affect functionality in others4, 5, 6-7....
作者：Momeni, Ali; Rahmani, Babak; Scellier, Benjamin; Wright, Logan G.; Mcmahon, Peter L.; Wanjura, Clara C.; Li, Yuhang; Skalli, Anas; Berloff, Natalia G.; Onodera, Tatsuhiro; Oguz, Ilker; Morichetti, Francesco; del Hougne, Philipp; Le Gallo, Manuel; Sebastian, Abu; Mirhoseini, Azalia; Zhang, Cheng; Markovic, Danijela; Brunner, Daniel; Moser, Christophe; Gigan, Sylvain; Marquardt, Florian; Ozcan, Aydogan; Grollier, Julie; Liu, Andrea J.; Psaltis, Demetri; Alu, Andrea; Fleury, Romain
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Microsoft; Microsoft United Kingdom; Yale University; Cornell University; Max Planck Society; University of California System; University of California Los Angeles; Universite Marie et Louis Pasteur; Universite de Technologie de Belfort-Montbeliard (UTBM); Centre National de la Recherche Scientifique (CNRS); University of Cambridge; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Polytechnic University of Milan; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Engineering & Systems Sciences (INSIS); Universite de Rennes; Stanford University; Alphabet Inc.; DeepMind; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); Institut Polytechnique de Paris; Ecole Polytechnique; Thales Group; Universite PSL; College de France; Ecole Normale Superieure (ENS); Centre National de la Recherche Scientifique (CNRS); Sorbonne Universite; Thales Group; Centre National de la Recherche Scientifique (CNRS); Universite Paris Saclay; University of Pennsylvania; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; City University of New York (CUNY) System; City University of New York (CUNY) System
摘要：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? Research over the past few years has shown that the a...
