The thermal deformation behavior of FV520B stainless steel is investigated.Isothermal compression tests were conducted at temperatures ranging from 600 to 900℃ and strain rates from 0.001 to 10 s^(−1).The true stress...The thermal deformation behavior of FV520B stainless steel is investigated.Isothermal compression tests were conducted at temperatures ranging from 600 to 900℃ and strain rates from 0.001 to 10 s^(−1).The true stress–strain curves were corrected for friction and temperature due to the drum shape and adiabatic heating.The comparison shows that there is a large difference between the stress before and after the correction,which proves that the correction is necessary.Five constitutive models were developed:the original Arrhenius model,the strain correction Arrhenius model,a new modified Arrhenius model,the back propagation neural network model(BPNN)and the dandelion optimization BPNN model(DO-BPNN).The DO-BPNN model showed the highest prediction accuracy though it was more computationally intensive than the other models.The new modified Arrhenius model performed a better predictive capacity than the strain correction version,while it showed a negligible increase in the number of parameters and computational time.Although artificial neural network-based models exhibit superior accuracy compared to the Arrhenius models,their application in finite element simulations still faces notable challenges.展开更多
A“water”accelerated metal-free catalytic system has been discovered for the Mukaiyama-aldol reaction.The system involves the use of B(C_(6)F_(5))_(3) as a catalyst,which is water-tolerant and able to activate the ca...A“water”accelerated metal-free catalytic system has been discovered for the Mukaiyama-aldol reaction.The system involves the use of B(C_(6)F_(5))_(3) as a catalyst,which is water-tolerant and able to activate the carbonyl group through a hydrogen bonding network generated by the catalyst.This activation method allows the reactions to be performed with very low catalyst loading,as low as 0.5 mol%.The scope of substrates is broad and a wide variety of functional groups are well tolerated.Diverse aliphatic aldehydes,aromatic aldehydes,unsaturated aldehydes and aromatic ketones coupled with silyl enol ethers/silyl ketone acetals to generate their correspondingβ-hydroxy carbonyl compounds in moderate to good yields.This discovery represents a significant advancement in the field of organic synthesis,as it provides a new,practical and sustainable solution for carbon-carbon bond formation in water.展开更多
Background:The precise insertion of large DNA fragments(>3–5 kb)remains one of the key obstacles in establishment of genetically modified murine models.Methods:A 21 kb large DNA fragment containing three tandemly ...Background:The precise insertion of large DNA fragments(>3–5 kb)remains one of the key obstacles in establishment of genetically modified murine models.Methods:A 21 kb large DNA fragment containing three tandemly linked copies of the human HRAS gene was inserted into the genome of C57BL/6J mouse,generating a mouse model designated as KI.C57-ras(or named NF-h HRAS).Whole-genome sequencing and Sanger sequencing were utilized to it confirm precise insertion and copy number.The stability of transgene expression among different generations was verified from multiple aspects using by digital PCR,western blot and DNA sequencing.To assess tumor susceptibility in the mouse model,N-Nitroso-N-methylurea(MNU)was administered at a dosage of 75 mg/kg.Histopathological examinations were conducted using hematoxylin and eosin(H&E)staining.Results:The HRAS DNA fragment was inserted into mouse chromosome 15E1 site,locating between 80623202 bp and 80625020 bp.NF-h HRAS mice exhibited stable inheritance and displayed consistent phenotypes across individuals.Moreover,this mouse model exhibited a high susceptibility to carcinogens.Upon administration of MNU the earliest mortality onset was earlier than that of wild-type littermates(day 65 vs.day 78 for male and day 56 vs.day 84 for female).Notably,100%of the NF-h HRAS transgenic mice developed tumors,with approximately 84%of male NF-h HRAS mice exhibiting specific tumor types,such as squamous cell carcinoma or squamous cell papilloma,which was consistent with the previously reported carcinogenic rasH2 mouse model.The types of tumors and the target organs exhibited diversity in NFh HRAS mice,while the spontaneous tumor incidence remained low(1/50).Conclusions:The NF-h HRAS mice demonstrated excellent genetic stability,a reproducible phenotype,and high susceptibility to carcinogens,indicating their potential utility in non-clinical safety evaluations of drugs as per the S1B guidelines issued by the ICH(The International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use).展开更多
Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patien...Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patients have been established,the mechanisms that drive these alterations remain incompletely understood.This study,which was conducted in 2018 at Northeastern University in China,included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset covering genetics,imaging,and clinical data.All participants were divided into two groups:normal control(n=52;20 males and 32 females;mean age 73.90±4.72 years)and Alzheimer’s disease(n=45,23 males and 22 females;mean age 74.85±5.66).To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients,we proposed a local naive Bayes brain network model based on graph theory.Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined,including clustering coefficient,modularity,characteristic path length,network efficiency,betweenness,and degree distribution compared with empirical methods.This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients.Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.The ADNI was performed in accordance with the Good Clinical Practice guidelines,US 21 CFR Part 50-Protection of Human Subjects,and Part 56-Institutional Review Boards(IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards(IRBs)/Research Ethics Boards(REBs).展开更多
The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digita...The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.展开更多
Based on the T-S fuzzy model,this paper presents a new model of non-linear network control system with stochastic transfer delay.Sufficient criterion is proposed to guarantee globally asymptotically stability of this ...Based on the T-S fuzzy model,this paper presents a new model of non-linear network control system with stochastic transfer delay.Sufficient criterion is proposed to guarantee globally asymptotically stability of this two-levels T-S fuzzy model.Also a T-S fuzzy observer of NCS is designed base on this two-levels T-S fuzzy model.All these results present a new approach for networked control system analysis and design.展开更多
The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities.The change tendency may be related to the occurrence of earthquake disasters...The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities.The change tendency may be related to the occurrence of earthquake disasters.Forecasting of the underlying intensity trend plays an important role in the analysis of data and disaster monitoring.Combining chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field signal.The main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field data.In verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data respectively.According to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function model.Hence,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.展开更多
Base on the principle and method of B-P neural network,the prediction model of SO2 concentration in urban atmosphere was established by using the statistical data of a city in southwest China from 1991 to 2009,so as t...Base on the principle and method of B-P neural network,the prediction model of SO2 concentration in urban atmosphere was established by using the statistical data of a city in southwest China from 1991 to 2009,so as to forecast atmospheric SO2 concentration in a city of southwest China.The results showed that B-P neural network applied in the prediction of SO2 concentration in urban atmosphere was reasonable and efficient with high accuracy and wide adaptability,so it was worthy to be popularized.展开更多
Among complex network models,the hierarchical network model is the one most close to such real networks as world trade web,metabolic network,WWW,actor network,and so on.It has not only the property of power-law degree...Among complex network models,the hierarchical network model is the one most close to such real networks as world trade web,metabolic network,WWW,actor network,and so on.It has not only the property of power-law degree distribution,but also the scaling clustering coefficient property which Barabási-Albert(BA)model does not have.BA model is a model of network growth based on growth and preferential attachment,showing the scale-free degree distribution property.In this paper,we study the evolution of cooperation on a hierarchical network model,adopting the prisoner's dilemma(PD)game and snowdrift game(SG)as metaphors of the interplay between connected nodes.BA model provides a unifying framework for the emergence of cooperation.But interestingly,we found that on hierarchical model,there is no sign of cooperation for PD game,while the fre-quency of cooperation decreases as the common benefit decreases for SG.By comparing the scaling clustering coefficient prop-erties of the hierarchical network model with that of BA model,we found that the former amplifies the effect of hubs.Considering different performances of PD game and SG on complex network,we also found that common benefit leads to cooperation in the evolution.Thus our study may shed light on the emergence of cooperation in both natural and social environments.展开更多
Rational planning of agricultural product transport route from initial node to destination node can effectively reduce the cost price of agricultural products,and the calculation of shortest path between any two point...Rational planning of agricultural product transport route from initial node to destination node can effectively reduce the cost price of agricultural products,and the calculation of shortest path between any two points also affects people’s daily travel.Taking Heze Railway Station to Heze College for example,with remote sensing image data as the base map,we conduct vectorization and topological analysis on roads in the target area.With Dijkstra as theoretical basis of shortest path algorithm,we use ArcG IS network analysis method to build road network,and calculate the planning program of the shortest distance path,the shortest path by driving and the shortest path by walking.展开更多
Security and reliability must be focused on control sys- tems firstly, and fault detection and diagnosis （FDD） is the main theory and technology. Now, there are many positive results in FDD for linear networked cont...Security and reliability must be focused on control sys- tems firstly, and fault detection and diagnosis （FDD） is the main theory and technology. Now, there are many positive results in FDD for linear networked control systems （LNCSs）, but nonlinear networked control systems （NNCSs） are less involved. Based on the T-S fuzzy-modeling theory, NNCSs are modeled and network random time-delays are changed into the unknown bounded uncertain part without changing its structure. Then a fuzzy state observer is designed and an observer-based fault detection approach for an NNCS is presented. The main results are given and the relative theories are proved in detail. Finally, some simulation results are given and demonstrate the proposed method is effective.展开更多
Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this wor...Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this work introduces a machine-learning-based,data-driven scheme to overcome the challenges encountered,with a trained neural network(NN)assuming the role of a surrogate model that captures the system’s dynamics and subsequently enables QOC to be performed on the NN instead of on the real system.The trained NN surrogate proves effective for practical QOC tasks and is further demonstrated to be adaptable to different experimental conditions,remaining robust across varying system sizes and pulse durations.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52275373)the National Natural Science Foundation of China(Grant No.52105397)the Open Foundation of National Key Laboratory of Metal Forming Technology and Heavy Equipment(Grant No.S2308100.W08).
文摘The thermal deformation behavior of FV520B stainless steel is investigated.Isothermal compression tests were conducted at temperatures ranging from 600 to 900℃ and strain rates from 0.001 to 10 s^(−1).The true stress–strain curves were corrected for friction and temperature due to the drum shape and adiabatic heating.The comparison shows that there is a large difference between the stress before and after the correction,which proves that the correction is necessary.Five constitutive models were developed:the original Arrhenius model,the strain correction Arrhenius model,a new modified Arrhenius model,the back propagation neural network model(BPNN)and the dandelion optimization BPNN model(DO-BPNN).The DO-BPNN model showed the highest prediction accuracy though it was more computationally intensive than the other models.The new modified Arrhenius model performed a better predictive capacity than the strain correction version,while it showed a negligible increase in the number of parameters and computational time.Although artificial neural network-based models exhibit superior accuracy compared to the Arrhenius models,their application in finite element simulations still faces notable challenges.
基金financial support from the Start-up Grant of Nanjing Tech University(Nos.38274017103,38037037)financial support from Distinguished University Professor grant(Nanyang Technological University)+1 种基金the Agency for Science,Technology and Research(A∗STAR)under its MTC Individual Research Grants(No.M21K2c0114)RIE2025 MTC Programmatic Fund(No.M22K9b0049).
文摘A“water”accelerated metal-free catalytic system has been discovered for the Mukaiyama-aldol reaction.The system involves the use of B(C_(6)F_(5))_(3) as a catalyst,which is water-tolerant and able to activate the carbonyl group through a hydrogen bonding network generated by the catalyst.This activation method allows the reactions to be performed with very low catalyst loading,as low as 0.5 mol%.The scope of substrates is broad and a wide variety of functional groups are well tolerated.Diverse aliphatic aldehydes,aromatic aldehydes,unsaturated aldehydes and aromatic ketones coupled with silyl enol ethers/silyl ketone acetals to generate their correspondingβ-hydroxy carbonyl compounds in moderate to good yields.This discovery represents a significant advancement in the field of organic synthesis,as it provides a new,practical and sustainable solution for carbon-carbon bond formation in water.
基金National Key R&D Program of China,Grant/Award Number:2023YFC3402000National Institutes for Food and Drug Control,State Key Laboratory of Drug Regulatory Science,Grant/Award Number:2023SKLDRS0124。
文摘Background:The precise insertion of large DNA fragments(>3–5 kb)remains one of the key obstacles in establishment of genetically modified murine models.Methods:A 21 kb large DNA fragment containing three tandemly linked copies of the human HRAS gene was inserted into the genome of C57BL/6J mouse,generating a mouse model designated as KI.C57-ras(or named NF-h HRAS).Whole-genome sequencing and Sanger sequencing were utilized to it confirm precise insertion and copy number.The stability of transgene expression among different generations was verified from multiple aspects using by digital PCR,western blot and DNA sequencing.To assess tumor susceptibility in the mouse model,N-Nitroso-N-methylurea(MNU)was administered at a dosage of 75 mg/kg.Histopathological examinations were conducted using hematoxylin and eosin(H&E)staining.Results:The HRAS DNA fragment was inserted into mouse chromosome 15E1 site,locating between 80623202 bp and 80625020 bp.NF-h HRAS mice exhibited stable inheritance and displayed consistent phenotypes across individuals.Moreover,this mouse model exhibited a high susceptibility to carcinogens.Upon administration of MNU the earliest mortality onset was earlier than that of wild-type littermates(day 65 vs.day 78 for male and day 56 vs.day 84 for female).Notably,100%of the NF-h HRAS transgenic mice developed tumors,with approximately 84%of male NF-h HRAS mice exhibiting specific tumor types,such as squamous cell carcinoma or squamous cell papilloma,which was consistent with the previously reported carcinogenic rasH2 mouse model.The types of tumors and the target organs exhibited diversity in NFh HRAS mice,while the spontaneous tumor incidence remained low(1/50).Conclusions:The NF-h HRAS mice demonstrated excellent genetic stability,a reproducible phenotype,and high susceptibility to carcinogens,indicating their potential utility in non-clinical safety evaluations of drugs as per the S1B guidelines issued by the ICH(The International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use).
基金Fundamental Research Funds for the Central Universities in China,No.N161608001 and No.N171903002
文摘Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patients have been established,the mechanisms that drive these alterations remain incompletely understood.This study,which was conducted in 2018 at Northeastern University in China,included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset covering genetics,imaging,and clinical data.All participants were divided into two groups:normal control(n=52;20 males and 32 females;mean age 73.90±4.72 years)and Alzheimer’s disease(n=45,23 males and 22 females;mean age 74.85±5.66).To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients,we proposed a local naive Bayes brain network model based on graph theory.Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined,including clustering coefficient,modularity,characteristic path length,network efficiency,betweenness,and degree distribution compared with empirical methods.This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients.Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.The ADNI was performed in accordance with the Good Clinical Practice guidelines,US 21 CFR Part 50-Protection of Human Subjects,and Part 56-Institutional Review Boards(IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards(IRBs)/Research Ethics Boards(REBs).
基金Project(E2015203354)supported by Natural Science Foundation of Steel United Research Fund of Hebei Province,ChinaProject(ZD2016100)supported by the Science and the Technology Research Key Project of High School of Hebei Province,China+1 种基金Project(LJRC013)supported by the University Innovation Team of Hebei Province Leading Talent Cultivation,ChinaProject(16LGY015)supported by the Basic Research Special Breeding of Yanshan University,China
文摘The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.
基金National Natural Science Foundation of china(60274014,60574088)
文摘Based on the T-S fuzzy model,this paper presents a new model of non-linear network control system with stochastic transfer delay.Sufficient criterion is proposed to guarantee globally asymptotically stability of this two-levels T-S fuzzy model.Also a T-S fuzzy observer of NCS is designed base on this two-levels T-S fuzzy model.All these results present a new approach for networked control system analysis and design.
基金sponsored by the National Natural Science Foundation of China(61333002)Open Research Foundation of the State Key Laboratory of Geodesy and Earth’s Dynamics(SKLGED2018-5-4-E)+5 种基金Foundation of the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems(ACIA2017002)111 projects under Grant(B17040)Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing(KLIGIP-2017A02)supported by the Three Gorges Research Center for geo-hazardMinistry of Education cooperation agreements of Krasnoyarsk Science Center and Technology BureauRussian Academy of Sciences。
文摘The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities.The change tendency may be related to the occurrence of earthquake disasters.Forecasting of the underlying intensity trend plays an important role in the analysis of data and disaster monitoring.Combining chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field signal.The main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field data.In verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data respectively.According to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function model.Hence,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.
文摘Base on the principle and method of B-P neural network,the prediction model of SO2 concentration in urban atmosphere was established by using the statistical data of a city in southwest China from 1991 to 2009,so as to forecast atmospheric SO2 concentration in a city of southwest China.The results showed that B-P neural network applied in the prediction of SO2 concentration in urban atmosphere was reasonable and efficient with high accuracy and wide adaptability,so it was worthy to be popularized.
基金Project supported by the Natural Science Foundation of ZhejiangProvince, China (No. Y105697)the Ningbo Natural ScienceFoundation,China (No. 2005A610004)
文摘Among complex network models,the hierarchical network model is the one most close to such real networks as world trade web,metabolic network,WWW,actor network,and so on.It has not only the property of power-law degree distribution,but also the scaling clustering coefficient property which Barabási-Albert(BA)model does not have.BA model is a model of network growth based on growth and preferential attachment,showing the scale-free degree distribution property.In this paper,we study the evolution of cooperation on a hierarchical network model,adopting the prisoner's dilemma(PD)game and snowdrift game(SG)as metaphors of the interplay between connected nodes.BA model provides a unifying framework for the emergence of cooperation.But interestingly,we found that on hierarchical model,there is no sign of cooperation for PD game,while the fre-quency of cooperation decreases as the common benefit decreases for SG.By comparing the scaling clustering coefficient prop-erties of the hierarchical network model with that of BA model,we found that the former amplifies the effect of hubs.Considering different performances of PD game and SG on complex network,we also found that common benefit leads to cooperation in the evolution.Thus our study may shed light on the emergence of cooperation in both natural and social environments.
基金Supported by National Natural Science Foundation of China （61034005, 60974071）, Program for New Century Excellent Talents in University （NCET-08-0101）, and Fundamental Research Funds for the Central Universities （N100104102, Nl10604007）
基金Supported by Science Foundation of Heze University(XY14SK14)
文摘Rational planning of agricultural product transport route from initial node to destination node can effectively reduce the cost price of agricultural products,and the calculation of shortest path between any two points also affects people’s daily travel.Taking Heze Railway Station to Heze College for example,with remote sensing image data as the base map,we conduct vectorization and topological analysis on roads in the target area.With Dijkstra as theoretical basis of shortest path algorithm,we use ArcG IS network analysis method to build road network,and calculate the planning program of the shortest distance path,the shortest path by driving and the shortest path by walking.
文摘Security and reliability must be focused on control sys- tems firstly, and fault detection and diagnosis （FDD） is the main theory and technology. Now, there are many positive results in FDD for linear networked control systems （LNCSs）, but nonlinear networked control systems （NNCSs） are less involved. Based on the T-S fuzzy-modeling theory, NNCSs are modeled and network random time-delays are changed into the unknown bounded uncertain part without changing its structure. Then a fuzzy state observer is designed and an observer-based fault detection approach for an NNCS is presented. The main results are given and the relative theories are proved in detail. Finally, some simulation results are given and demonstrate the proposed method is effective.
基金supported by the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302100)the National Natural Science Foundation of China(Grant Nos.12361131576,92265205,and 92476205).
文摘Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this work introduces a machine-learning-based,data-driven scheme to overcome the challenges encountered,with a trained neural network(NN)assuming the role of a surrogate model that captures the system’s dynamics and subsequently enables QOC to be performed on the NN instead of on the real system.The trained NN surrogate proves effective for practical QOC tasks and is further demonstrated to be adaptable to different experimental conditions,remaining robust across varying system sizes and pulse durations.
