Research Progress in Metamaterial Design and Fiber Beam Control Based on Deep Learning br

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There, hers have samad ja des korning methods, atting i use a disdriven spachta allow jud turk malale learn the mapping relationship between metamaterial simriure at itical response during the leature hearing pics, this achieving seen and fficient metal design while shielding malerging physical details. Fiber been truliers in mijasting parmers such as plade, phe, id pariation fiber uptle beam nuhtain novel Strahlt. Traditional metabixinly inchale getir algurithms, chic palled grade dem (SPGI algorithms, PIT), and there mihols, which ur mind by their inlay thinly slee si ramiral problems in complex environments, he spend and say bos. These uptimeation metals he sings strangies that are made to gerg lahvinst paths, resulting in has many steps to the target. Mor, day chirally respond in environmental state, making them inerable in system and liming the accuracy of out. 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Purik et al. med MIP in complete the inverse design ul-layer spherical shell nature (Fig. 7a Lietu proposed methink that mombines and indict works for space design networks decies (Fig. 7th D w /developed a sodalile tuultitask leaming (SMTL) model for designing finedisional tractures (Fig. 7) Zatul led dutiestrial itiative lewample (DEIPS) algorithm bad in dais gmentation (Fig 7d) In wit MIP tamenibutional neural ack (CNN) and generative alertal network (GAN) are by aid atk malls, Zhu et al. powder learning bed and for predicting metaneriais seurately and quickly wing the pre-trained Inception VS munde) con image date, achieving good realis lur hinary stamterial pret Fig Bil Jiang et al. med GAN the ing desigs, ne rumplex nudeviers, lectively sung the profilem af timing Herative optimization methods for design compile is the virus, veluting slewig time les suur 80% [Fig. 8) Sajedio et al, efficiently determined the optional parameters fr Hove-layer metresberial devices in 25 dibial types and get params using the shouble dorp Q network (DDQN), gmaily imprising the computational tracesficiency almerinis Fig. Het het at grant the alex inf weiskimement learning any the model and designed and do greedy optimization (EDGO algorithun Fig. 854)) Sejadian at ul. sumbinal CNN with neural network (RNS) in perdiet the slurption sperten af smileviews, which played auxiliary mile in desce design Fig. Well In her heam contral, the 1 N. Kun eam at the Universay i Wshingum propused, uning damp rendimement learning algorithmne tu achieve automutir muiste kuking emal of lasers from a simulation purspective in 2020 (Fig. 1all In 2001, the t nd by her Fang Tian at the National University of This Tengy dolged in automatic cale, locking coll tem bad in the being sad the BELAY sing algorithm. Fig. 1b). Ju mid-2022, Li Zhan et al. In the Chiese Academy of Sciemsee design levickel algorithm in deep in learning and hing memory LSTM alle biller, the state of malked a Fig. 1). In the later half of 2002. IS etalum Nanjing Liversity of Science and Teckningy the TDS algorithms thesprometinga ulirane) HZ.AN (Fig. 1. In 2009, the call by Jing Tim at the National University of Def Technology RCON ng inflaming the stability of coherent prical neurum systems (Fig. 1). Conclusion and Prospect Thi sesc un deep learning material design and fiber betr The nation of deep lining gray pumu development of both Belle Trafal meshes her the following problems when deading with increasingly complex upticao v & drangbrac; (1) inability to effectively maler expert experienes, (2) indility to dimrical calculatima; and (3) fired solvable p van hellation the lerlying phys vinilo 1 spart. Compared with readinimal methods, sleep leuning methoude mbing the difficulty if design id

Automated summary

This article introduces the importance of material design and fiber beams, as well as the application of deep learning algorithms in material design. Through deep learning algorithms, the performance and response of materials can be effectively simulated, achieving efficient material design.