As an unsupervised learning method,stochastic competitive learning is commonly used for community detection in social network analysis.Compared with the traditional community detection algorithms,it has the advantage ...As an unsupervised learning method,stochastic competitive learning is commonly used for community detection in social network analysis.Compared with the traditional community detection algorithms,it has the advantage of realizing the time-series community detection by simulating the community formation process.In order to improve the accuracy and solve the problem that several parameters in stochastic competitive learning need to be pre-set,the author improves the algorithms and realizes improved stochastic competitive learning by particle position initialization,parameter optimization and particle domination ability self-adaptive.The experiment result shows that each improved method improves the accuracy of the algorithm,and the F1 score of the improved algorithm is 9.07%higher than that of original algorithm.展开更多
Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularl...Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.展开更多
Satellite communication has been seen as a vital part of the sixth generation communication,which greatly extends network coverage.In satellite communication,resource management is a key problem attracting many resear...Satellite communication has been seen as a vital part of the sixth generation communication,which greatly extends network coverage.In satellite communication,resource management is a key problem attracting many research interests.However,previous study mainly focuses on throughput improvement via power allocation and spectrum assignment and the proposed approaches are mostly model-based and dedicated to specific problem structures.Fortunately,with the trend of edge intelligence,complex resource management problems can be efficiently resolved in a model-free manner.In this paper,a joint beam activation,user-beam association and time resource allocation approach is proposed.The core idea is using stochastic learning at the ground station to identify active user-link beams to meet user rate demand.In addition,the convergence,optimality and complexity of our proposal are rigorously discussed.By simulation,it is shown that the rate goal of most of the users can be met and meanwhile satellite energy is saved owing to much less active beams.展开更多
With the rapid increase of observational,experimental and simulated data for stochastic systems,tremendous efforts have been devoted to identifying governing laws underlying the evolution of these systems.Despite the ...With the rapid increase of observational,experimental and simulated data for stochastic systems,tremendous efforts have been devoted to identifying governing laws underlying the evolution of these systems.Despite the broad applications of non-Gaussian fluctuations in numerous physical phenomena,the data-driven approaches to extracting stochastic dynamics with Levy noise are relatively few.In this work,we propose aWeak Collocation Regression(WCR)to explicitly reveal unknown stochastic dynamical systems,i.e.,the Stochastic Differential Equation(SDE)with bothα-stable Levy noise and Gaussian noise,from discrete aggregate data.This method utilizes the evolution equation of the probability distribution function,i.e.,the Fokker-Planck(FP)equation.With the weak form of the FP equation,the WCR constructs a linear system of unknown parameters where all integrals are evaluated by Monte Carlo method with the observations.Then,the unknown parameters are obtained by a sparse linear regression.For a SDE with Levy noise,the corresponding FP equation is a partial integro-differential equation(PIDE),which contains nonlocal terms,and is difficult to deal with.The weak form can avoid complicated multiple integrals.Our approach can simultaneously distinguish mixed noise types,even in multi-dimensional problems.Numerical experiments demonstrate that our method is accurate and computationally efficient.展开更多
基金This research was funded by National Natural Science Foundation of China(Grant No.2017YFC0820100)。
文摘As an unsupervised learning method,stochastic competitive learning is commonly used for community detection in social network analysis.Compared with the traditional community detection algorithms,it has the advantage of realizing the time-series community detection by simulating the community formation process.In order to improve the accuracy and solve the problem that several parameters in stochastic competitive learning need to be pre-set,the author improves the algorithms and realizes improved stochastic competitive learning by particle position initialization,parameter optimization and particle domination ability self-adaptive.The experiment result shows that each improved method improves the accuracy of the algorithm,and the F1 score of the improved algorithm is 9.07%higher than that of original algorithm.
文摘Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.
基金supported in part by the National Natural Science Foundation of China(No.62001053)the Beijing Municipal Science and Technology Project(No.Z211100004421017)Young Elite Scientist Sponsorship Program by China Institute of Communications.
文摘Satellite communication has been seen as a vital part of the sixth generation communication,which greatly extends network coverage.In satellite communication,resource management is a key problem attracting many research interests.However,previous study mainly focuses on throughput improvement via power allocation and spectrum assignment and the proposed approaches are mostly model-based and dedicated to specific problem structures.Fortunately,with the trend of edge intelligence,complex resource management problems can be efficiently resolved in a model-free manner.In this paper,a joint beam activation,user-beam association and time resource allocation approach is proposed.The core idea is using stochastic learning at the ground station to identify active user-link beams to meet user rate demand.In addition,the convergence,optimality and complexity of our proposal are rigorously discussed.By simulation,it is shown that the rate goal of most of the users can be met and meanwhile satellite energy is saved owing to much less active beams.
基金supported by the National Key R&D Program of China(Grant No.2021YFA0719200).
文摘With the rapid increase of observational,experimental and simulated data for stochastic systems,tremendous efforts have been devoted to identifying governing laws underlying the evolution of these systems.Despite the broad applications of non-Gaussian fluctuations in numerous physical phenomena,the data-driven approaches to extracting stochastic dynamics with Levy noise are relatively few.In this work,we propose aWeak Collocation Regression(WCR)to explicitly reveal unknown stochastic dynamical systems,i.e.,the Stochastic Differential Equation(SDE)with bothα-stable Levy noise and Gaussian noise,from discrete aggregate data.This method utilizes the evolution equation of the probability distribution function,i.e.,the Fokker-Planck(FP)equation.With the weak form of the FP equation,the WCR constructs a linear system of unknown parameters where all integrals are evaluated by Monte Carlo method with the observations.Then,the unknown parameters are obtained by a sparse linear regression.For a SDE with Levy noise,the corresponding FP equation is a partial integro-differential equation(PIDE),which contains nonlocal terms,and is difficult to deal with.The weak form can avoid complicated multiple integrals.Our approach can simultaneously distinguish mixed noise types,even in multi-dimensional problems.Numerical experiments demonstrate that our method is accurate and computationally efficient.
