Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current t...Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.展开更多
This study presents a novel neural network architecture called spectral integrated neural networks(SINNs),which combines physics-informed neural networks(PINNs)with time-spectral integration techniques to efficiently ...This study presents a novel neural network architecture called spectral integrated neural networks(SINNs),which combines physics-informed neural networks(PINNs)with time-spectral integration techniques to efficiently solve two-and three-dimensional dynamic piezoelectric problems.To avoid the numerical instability associated with time-differential operators,the coupled system of mechanical and electrical equilibrium equations is reformulated into a weak time-integral form.The temporal derivatives of displacement and voltage fields,treated as the primary unknown physical quantities,can be approximated utilizing fully connected neural networks(FCNNs).The displacements and electric potential are subsequently recovered through time-spectral integration of their respective derivatives.A physical-informed loss function is formulated by the weak time-integral type of the governing equations and boundary conditions,with the initial conditions embedded within the integral expressions.The proposed SINNs demonstrate superior stability and accuracy,even under large time steps conditions.Numerical verification is accomplished through three representative test cases of the method,and a comparison analysis is presented between the results obtained by the SINNs and those from the PINNs.展开更多
The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and n...The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given. It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.展开更多
This paper presents a novel approach called the boundary integrated neural networks(BINNs)for analyzing acoustic radiation and scattering.The method introduces fundamental solutions of the time-harmonic wave equation ...This paper presents a novel approach called the boundary integrated neural networks(BINNs)for analyzing acoustic radiation and scattering.The method introduces fundamental solutions of the time-harmonic wave equation to encode the boundary integral equations(BIEs)within the neural networks,replacing the conventional use of the governing equation in physics-informed neural networks(PINNs).This approach offers several advantages.First,the input data for the neural networks in the BINNs only require the coordinates of“boundary”collocation points,making it highly suitable for analyzing acoustic fields in unbounded domains.Second,the loss function of the BINNs is not a composite form and has a fast convergence.Third,the BINNs achieve comparable precision to the PINNs using fewer collocation points and hidden layers/neurons.Finally,the semianalytic characteristic of the BIEs contributes to the higher precision of the BINNs.Numerical examples are presented to demonstrate the performance of the proposed method,and a MATLAB code implementation is provided as supplementary material.展开更多
Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DN...Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis.展开更多
Rising demands for bandwidth,speed,and energy efficiency are reshaping the landscape of computing beyond the limits of von Neumann electronics.Neuromorphic photonics—using light to emulate neural computation—offers ...Rising demands for bandwidth,speed,and energy efficiency are reshaping the landscape of computing beyond the limits of von Neumann electronics.Neuromorphic photonics—using light to emulate neural computation—offers ultrafast,massively parallel,and low-energy information processing,positioning integrated photonic neural networks(IPNNs)as promising hardware for next-generation artificial intelligence(AI).By combining the architectural efficiency of neuromorphic models with the physical advantages of integrated photonics,IPNNs enable high-speed and programmable linear operations during the in-plane optical transmission,while leaving room for compact and reconfigurable on-chip optical nonlinearities and memory functions.Firstly,we review the concepts and principles of key building blocks in IPNN,that are photonic synapses,neurons,and photonic memristors which offer optical memory and storage capabilities.And then,we summarize the representative IPNN architectures and their recent advances,including coherent,parallel,diffractive,and reservoir computing,for photonic neuromorphic computing with high throughput and high efficiency.Finally,we outline practical considerations—calibration and stability of large-scale networks,routes toward co-integration with electronics,diffractive–interferometric hybrid architectures,and programmable photonic architectures for general AI purposes.We highlight a forward outlook on enabling IPNN with low energy consumption,robust photonic operations,and efficient training strategies,aiming to guide the maturation of general-purpose,low-power photonic AI.展开更多
Amorphous and non-stoichiometric hafnium oxide(a-HfO_(x))systems are essential for advanced electronic applications due to their superior electrical properties.Simulating their atomic behaviors under electric fields(E...Amorphous and non-stoichiometric hafnium oxide(a-HfO_(x))systems are essential for advanced electronic applications due to their superior electrical properties.Simulating their atomic behaviors under electric fields(Efield)is critical but challenging.Ab-initio molecular dynamics(AIMD)offer high accuracy but is computationally expensive,while classical MD lacks precision.To address this,we develop a charge equilibration integrated graph neural network(CIGNN)model that predicts atomic charge,energy,and force under Efield conditions.Using the CIGNN model and AIMD datasets,we develop a CIGNN-based machine learning potential(CNMP)optimized for a-HfO_(x)systems.The CNMP achieves quantum mechanical accuracy and effectively captures the atomic behaviors and dynamic properties of these systems across varying temperatures,densities,and E_(field)conditions.We expect the CNMP to serve as a valuable tool for studying field-induced phenomena in complex systems and to provide a foundation for advancing innovations in electronic applications.展开更多
For complex systems with high nonlinearity and strong coupling,the decoupling control technology based on proportion integration differentiation(PID)neural network(PIDNN)is used to eliminate the coupling between loops...For complex systems with high nonlinearity and strong coupling,the decoupling control technology based on proportion integration differentiation(PID)neural network(PIDNN)is used to eliminate the coupling between loops.The connection weights of the PIDNN are easy to fall into local optimum due to the use of the gradient descent learning method.In order to solve this problem,a hybrid particle swarm optimization(PSO)and differential evolution(DE)algorithm(PSO-DE)is proposed for optimizing the connection weights of the PIDNN.The DE algorithm is employed as an acceleration operation to help the swarm to get out of local optima traps in case that the optimal result has not been improved after several iterations.Two multivariable controlled plants with strong coupling between input and output pairs are employed to demonstrate the effectiveness of the proposed method.Simulation results show t hat the proposed met hod has better decoupling capabilities and control quality than the previous approaches.展开更多
Integrated diffractive optical neural networks(DONNs)have significant potential for complex machine learning tasks with high speed and ultralow energy consumption.However,the on-chip implementation of a high-performan...Integrated diffractive optical neural networks(DONNs)have significant potential for complex machine learning tasks with high speed and ultralow energy consumption.However,the on-chip implementation of a high-performance optical neural network is limited by input dimensions.In contrast to existing photonic neural networks,a space-time interleaving technology based on arrayed waveguides is designed to realize an on-chip DONN with high-speed,high-dimensional,and all-optical input signal modulation.To demonstrate the performance of the on-chip DONN with high-speed space-time interleaving modulation,an on-chip DONN with a designed footprint of 0.0945 mm~2is proposed to resolve the vowel recognition task,reaching a computation speed of about 1.4×10^(13)operations per second and yielding an accuracy of 98.3%in numerical calculation.In addition,the function of the specially designed arrayed waveguides for realizing parallel signal inputs using space-time conversion has been verified experimentally.This method can realize the on-chip DONN with higher input dimension and lower energy consumption.展开更多
With the rapid development of information technology,artificial intelligence and large-scale models have exhibited exceptional performance and widespread applications.Photonic hardware offers a promising solution to m...With the rapid development of information technology,artificial intelligence and large-scale models have exhibited exceptional performance and widespread applications.Photonic hardware offers a promising solution to meet the growing demands for computational power and energy efficiency.Researchers have aimed to develop an efficient integrated photonic computing chip capable of supporting a wide range of application scenarios in both static and dynamic temporal domains.However,with several mainstream photonic components already well-developed,achieving fundamental breakthroughs at the level of basic computing units remains highly challenging.Here,we report a novel algorithm-hardware co-design strategy that enables in situ reconfigurability across diverse neural network models,all within a unified photonic configuration.We unlock the intrinsic capabilities of a compact cross-waveguide coupled microring component to natively support both static and dynamic temporal tasks.As a proof of concept,we experimentally integrated a turnkey soliton microcomb as the light source on the photonic computing platform,demonstrating the realization of fully connected,convolutional,and recurrent neural network models within a unified structure.The chip achieves area computing efficiency of up to 2.45 TOPS/mm^(2) for 208 tunable components.We evaluate the performance of the proposed chip by implementing image classification tasks on the MNIST and CIFAR-10 datasets,achieving measured test accuracies of 92.93%and 56.57%,respectively.Sentiment analysis on the IMDB dataset achieves a measured test accuracy of 80.81%.Furthermore,speech recognition is implemented by combining three neural networks within a scaled-up architecture.This work addresses the challenges of performing versatile computations on integrated photonic platforms,offering a promising solution for chipintegrated multifunctional photonic information processing.展开更多
In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response...In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response surface,and the genetic algorithm.First,a multi-step press bend forming FEM equivalent model was established,with which the FEM experiments designed with Taguchi method were performed.Then,the BP neural network response surface was developed with the sample data from the FEM experiments.Furthermore,genetic algorithm was applied with the neural network response surface as the objective function. Finally,verification was carried out on a simple curvature grid-type stiffened panel.The forming error of the panel formed with the optimal path is only 0.098 39 and the calculating efficiency has been improved by 77%.Therefore,this novel optimization method is quite efficient and indispensable for the press bend forming path designing.展开更多
基金the North Dakota Industrial Commission (NDIC) for their financial supportprovided by the University of North Dakota Computational Research Center。
文摘Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.
基金funded by the Natural Science Foundation of Shandong Province of China,Grant/Award Numbers:ZR2022YQ06,ZR2024MA002Development Plan of Youth Innovation Team in Colleges and Universities of Shandong Province,Grant/Award Number:2022KJ140+1 种基金National Natural Science Foundation of China,Grant/Award Numbers:12372199,12422207Key Laboratory of Road Construction Technology and Equipment,Grant/Award Number:300102253502.
文摘This study presents a novel neural network architecture called spectral integrated neural networks(SINNs),which combines physics-informed neural networks(PINNs)with time-spectral integration techniques to efficiently solve two-and three-dimensional dynamic piezoelectric problems.To avoid the numerical instability associated with time-differential operators,the coupled system of mechanical and electrical equilibrium equations is reformulated into a weak time-integral form.The temporal derivatives of displacement and voltage fields,treated as the primary unknown physical quantities,can be approximated utilizing fully connected neural networks(FCNNs).The displacements and electric potential are subsequently recovered through time-spectral integration of their respective derivatives.A physical-informed loss function is formulated by the weak time-integral type of the governing equations and boundary conditions,with the initial conditions embedded within the integral expressions.The proposed SINNs demonstrate superior stability and accuracy,even under large time steps conditions.Numerical verification is accomplished through three representative test cases of the method,and a comparison analysis is presented between the results obtained by the SINNs and those from the PINNs.
文摘The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given. It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.
基金Natural Science Foundation of Shandong Province of China,Grant/Award Numbers:ZR2022YQ06,ZR2021JQ02Development Plan of Youth Innovation Team in Colleges and Universities of Shandong Province,Grant/Award Number:2022KJ140+2 种基金National Natural Science Foundation of China,Grant/Award Number:12372199Fund of the Key Laboratory of Road Construction Technology and Equipment,Chang'an University,Grant/Award Number:300102253502Water Affairs Technology Project of Nanjing,Grant/Award Number:202203。
文摘This paper presents a novel approach called the boundary integrated neural networks(BINNs)for analyzing acoustic radiation and scattering.The method introduces fundamental solutions of the time-harmonic wave equation to encode the boundary integral equations(BIEs)within the neural networks,replacing the conventional use of the governing equation in physics-informed neural networks(PINNs).This approach offers several advantages.First,the input data for the neural networks in the BINNs only require the coordinates of“boundary”collocation points,making it highly suitable for analyzing acoustic fields in unbounded domains.Second,the loss function of the BINNs is not a composite form and has a fast convergence.Third,the BINNs achieve comparable precision to the PINNs using fewer collocation points and hidden layers/neurons.Finally,the semianalytic characteristic of the BIEs contributes to the higher precision of the BINNs.Numerical examples are presented to demonstrate the performance of the proposed method,and a MATLAB code implementation is provided as supplementary material.
基金National Natural Science Foundation of China(Nos.11262014,11962021 and 51965051)Inner Mongolia Natural Science Foundation,China(No.2019MS05064)+1 种基金Inner Mongolia Earthquake Administration Director Fund Project,China(No.2019YB06)Inner Mongolia University of Technology Foundation,China(No.2020015)。
文摘Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis.
基金supported by the Shanghai Municipal Science and Technology Major Projectthe Science and Technology Commission of Shanghai Municipality(No.21DZ1100500)+3 种基金the Shanghai Frontiers Science Center Program(2021-2025 No.20)the National Key Research and Development Program of China(No.2021YFB2802000)the National Natural Science Foundation of China(No.61975123,No.62305217)the National Natural Science Foundation of China(No.52075504),and the Shanghai Pujiang Programme.
文摘Rising demands for bandwidth,speed,and energy efficiency are reshaping the landscape of computing beyond the limits of von Neumann electronics.Neuromorphic photonics—using light to emulate neural computation—offers ultrafast,massively parallel,and low-energy information processing,positioning integrated photonic neural networks(IPNNs)as promising hardware for next-generation artificial intelligence(AI).By combining the architectural efficiency of neuromorphic models with the physical advantages of integrated photonics,IPNNs enable high-speed and programmable linear operations during the in-plane optical transmission,while leaving room for compact and reconfigurable on-chip optical nonlinearities and memory functions.Firstly,we review the concepts and principles of key building blocks in IPNN,that are photonic synapses,neurons,and photonic memristors which offer optical memory and storage capabilities.And then,we summarize the representative IPNN architectures and their recent advances,including coherent,parallel,diffractive,and reservoir computing,for photonic neuromorphic computing with high throughput and high efficiency.Finally,we outline practical considerations—calibration and stability of large-scale networks,routes toward co-integration with electronics,diffractive–interferometric hybrid architectures,and programmable photonic architectures for general AI purposes.We highlight a forward outlook on enabling IPNN with low energy consumption,robust photonic operations,and efficient training strategies,aiming to guide the maturation of general-purpose,low-power photonic AI.
基金supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning No. NRF-2020R1A6C101A202 and NRF-2024M3A7C2045166 and NRF-2021M3I3A1084940 and RS-2023-00257666 and RS-2024-00446683 and RS-2024-00450836.
文摘Amorphous and non-stoichiometric hafnium oxide(a-HfO_(x))systems are essential for advanced electronic applications due to their superior electrical properties.Simulating their atomic behaviors under electric fields(Efield)is critical but challenging.Ab-initio molecular dynamics(AIMD)offer high accuracy but is computationally expensive,while classical MD lacks precision.To address this,we develop a charge equilibration integrated graph neural network(CIGNN)model that predicts atomic charge,energy,and force under Efield conditions.Using the CIGNN model and AIMD datasets,we develop a CIGNN-based machine learning potential(CNMP)optimized for a-HfO_(x)systems.The CNMP achieves quantum mechanical accuracy and effectively captures the atomic behaviors and dynamic properties of these systems across varying temperatures,densities,and E_(field)conditions.We expect the CNMP to serve as a valuable tool for studying field-induced phenomena in complex systems and to provide a foundation for advancing innovations in electronic applications.
基金This work was supported by the Key Project of Chinese Ministry of Education(No.212135)the Guangxi Natural Science Foundation(No.2012GXNSFBA053165)+1 种基金the Projec t of Education Department of Guangxi(No.201203YB131)the Project of Guangxi Key Laboratory(No.14-045-44)。
文摘For complex systems with high nonlinearity and strong coupling,the decoupling control technology based on proportion integration differentiation(PID)neural network(PIDNN)is used to eliminate the coupling between loops.The connection weights of the PIDNN are easy to fall into local optimum due to the use of the gradient descent learning method.In order to solve this problem,a hybrid particle swarm optimization(PSO)and differential evolution(DE)algorithm(PSO-DE)is proposed for optimizing the connection weights of the PIDNN.The DE algorithm is employed as an acceleration operation to help the swarm to get out of local optima traps in case that the optimal result has not been improved after several iterations.Two multivariable controlled plants with strong coupling between input and output pairs are employed to demonstrate the effectiveness of the proposed method.Simulation results show t hat the proposed met hod has better decoupling capabilities and control quality than the previous approaches.
基金supported by the National Natural Science Foundation of China(NSFC)(No.62135009)the Beijing Municipal Science&Technology Commission,Administrative Commission of Zhongguancun Science Park(No.Z221100005322010)。
文摘Integrated diffractive optical neural networks(DONNs)have significant potential for complex machine learning tasks with high speed and ultralow energy consumption.However,the on-chip implementation of a high-performance optical neural network is limited by input dimensions.In contrast to existing photonic neural networks,a space-time interleaving technology based on arrayed waveguides is designed to realize an on-chip DONN with high-speed,high-dimensional,and all-optical input signal modulation.To demonstrate the performance of the on-chip DONN with high-speed space-time interleaving modulation,an on-chip DONN with a designed footprint of 0.0945 mm~2is proposed to resolve the vowel recognition task,reaching a computation speed of about 1.4×10^(13)operations per second and yielding an accuracy of 98.3%in numerical calculation.In addition,the function of the specially designed arrayed waveguides for realizing parallel signal inputs using space-time conversion has been verified experimentally.This method can realize the on-chip DONN with higher input dimension and lower energy consumption.
基金funded by National Key Research and Development Program of China(2024YFA1209204)National Natural Science Foundation of China(12474322,62274179,62235001)+3 种基金Innovation Program for Quantum Science and Technology(2021ZD0301500)Beijing Natural Science Foundation(Z210004)International Partnership Program of Chinese Academy of Sciences(02GJHZ2023026FN)173 Technical Field Fund(2023-JCJQ-JJ-1017,2022-JCJQ-JJ-0446).
文摘With the rapid development of information technology,artificial intelligence and large-scale models have exhibited exceptional performance and widespread applications.Photonic hardware offers a promising solution to meet the growing demands for computational power and energy efficiency.Researchers have aimed to develop an efficient integrated photonic computing chip capable of supporting a wide range of application scenarios in both static and dynamic temporal domains.However,with several mainstream photonic components already well-developed,achieving fundamental breakthroughs at the level of basic computing units remains highly challenging.Here,we report a novel algorithm-hardware co-design strategy that enables in situ reconfigurability across diverse neural network models,all within a unified photonic configuration.We unlock the intrinsic capabilities of a compact cross-waveguide coupled microring component to natively support both static and dynamic temporal tasks.As a proof of concept,we experimentally integrated a turnkey soliton microcomb as the light source on the photonic computing platform,demonstrating the realization of fully connected,convolutional,and recurrent neural network models within a unified structure.The chip achieves area computing efficiency of up to 2.45 TOPS/mm^(2) for 208 tunable components.We evaluate the performance of the proposed chip by implementing image classification tasks on the MNIST and CIFAR-10 datasets,achieving measured test accuracies of 92.93%and 56.57%,respectively.Sentiment analysis on the IMDB dataset achieves a measured test accuracy of 80.81%.Furthermore,speech recognition is implemented by combining three neural networks within a scaled-up architecture.This work addresses the challenges of performing versatile computations on integrated photonic platforms,offering a promising solution for chipintegrated multifunctional photonic information processing.
基金Project(20091102110021)supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China
文摘In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response surface,and the genetic algorithm.First,a multi-step press bend forming FEM equivalent model was established,with which the FEM experiments designed with Taguchi method were performed.Then,the BP neural network response surface was developed with the sample data from the FEM experiments.Furthermore,genetic algorithm was applied with the neural network response surface as the objective function. Finally,verification was carried out on a simple curvature grid-type stiffened panel.The forming error of the panel formed with the optimal path is only 0.098 39 and the calculating efficiency has been improved by 77%.Therefore,this novel optimization method is quite efficient and indispensable for the press bend forming path designing.
