We aimed at the release and dynamic management of CAD network graphics library (NGL). The characteristics of realization on network of CAD graphics are analysed, while the existing problems of the presenting share met...We aimed at the release and dynamic management of CAD network graphics library (NGL). The characteristics of realization on network of CAD graphics are analysed, while the existing problems of the presenting share methods of graphics file are also discussed. Release and dynamic management are accomplished with the B/S combined with C/S as well as the file organization based on attribute information, which have essential practical sense to the establishment of CAD NGL, share and cooperation in tech-design as well as the distance education of engineering graphics.展开更多
Introduction EQUATOR Network provides unique access to collated expertise and resources for good reporting of health research, The resources are aimed at researchers （authors of research articles）, journal editors, ...Introduction EQUATOR Network provides unique access to collated expertise and resources for good reporting of health research, The resources are aimed at researchers （authors of research articles）, journal editors, peer reviewers, and developers of reporting guidelines,展开更多
with the development of the digital information environment, library education has gradually become one of the forms of social recognition. This paper expounds the connotation, features, functions and contents of libr...with the development of the digital information environment, library education has gradually become one of the forms of social recognition. This paper expounds the connotation, features, functions and contents of library education, and constructs the mode of Library Education in the new period.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
文摘We aimed at the release and dynamic management of CAD network graphics library (NGL). The characteristics of realization on network of CAD graphics are analysed, while the existing problems of the presenting share methods of graphics file are also discussed. Release and dynamic management are accomplished with the B/S combined with C/S as well as the file organization based on attribute information, which have essential practical sense to the establishment of CAD NGL, share and cooperation in tech-design as well as the distance education of engineering graphics.
文摘Introduction EQUATOR Network provides unique access to collated expertise and resources for good reporting of health research, The resources are aimed at researchers （authors of research articles）, journal editors, peer reviewers, and developers of reporting guidelines,
文摘with the development of the digital information environment, library education has gradually become one of the forms of social recognition. This paper expounds the connotation, features, functions and contents of library education, and constructs the mode of Library Education in the new period.
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.
