Research Progress in Metamaterial Design and Fiber Beam Control Based on Deep Learning br
CHINESE JOURNAL OF LASERS-ZHONGGUO JIGUANG(2023)
Abstract
Significance Meterial design mad fiber beam indral in important topics in the study of aprical fachd manipulatain
Milicial material with pertudie were sud physical profice that do not mumlly in the world. Suitable structural designs, weil fhieving the potential of materials Numerical calculatis und parameter sptitaication nasthunde Site Efference time dimas (FDTD), methid (FEM), rigas piled wave analysis (FWA anal genetic algierskens are remedy used materials design. However, the invitate suller fram high computational es mal wiring thegendence im expert experience. Sperially, the high engastical cat is due to the complexity of eling panal dillereal sequations, while the line in experience atem fer the the numerical frulation methods depend physical midling Alitally printer miss algurithms las let high computational costs des to the spineam af parater condenations reputed calle trial amputation mituds. 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. Th rendent leaming overcomes these limitations by naluring leaning mucho that em actively not mental stimuli, making op Sath beings of traditional the Filer ham intrat is city proces that a state machine, which is usually suited for mintha hased the rainforest sing. Then, for such or even more tumpriro systemis, deep zwiskowement leaning-hused methinde have conuiderable application proquets.
Progress Mayer pepo (ML.Peale uml setwork mistel widely mood in vations utsmaterial design
wirks. 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 fine"disional 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
MoreMilicial material with pertudie were sud physical profice that do not mumlly in the world. Suitable structural designs, weil fhieving the potential of materials Numerical calculatis und parameter sptitaication nasthunde Site Efference time dimas (FDTD), methid (FEM), rigas piled wave analysis (FWA anal genetic algierskens are remedy used materials design. However, the invitate suller fram high computational es mal wiring thegendence im expert experience. Sperially, the high engastical cat is due to the complexity of eling panal dillereal sequations, while the line in experience atem fer the the numerical frulation methods depend physical midling Alitally printer miss algurithms las let high computational costs des to the spineam af parater condenations reputed calle trial amputation mituds. 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. Th rendent leaming overcomes these limitations by naluring leaning mucho that em actively not mental stimuli, making op Sath beings of traditional the Filer ham intrat is city proces that a state machine, which is usually suited for mintha hased the rainforest sing. Then, for such or even more tumpriro systemis, deep zwiskowement leaning-hused methinde have conuiderable application proquets.
Progress Mayer pepo (ML.Peale uml setwork mistel widely mood in vations utsmaterial design
wirks. 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 fine"disional 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
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Key words
materials,fiber optics,neural networks,optical field manipulation,metamaterial design,optical system control
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