Library transformation in the post-knowledge service era

The transformation is not only the main characteristic of the contemporary librarianship,but also the focus of the theoretical research of the library science.At present,the library transformation is in the post-knowledge service era.In such an era,innovation has become the norm,and transformation has become a topic for discussion.Libraries need to take the initiative to transform.Library transformation is a system.In addition to the profound influence of social environment and industry environment beyond the system,external and internal driving forces exist,which form a joint force to promote the transformation and development of the library.In the post-knowledge service-era,library transformation is composed of four elements:space,resources,services and management.The overall transformation,which is related to the target orientation of the next generation of libraries,is the result of the transformation of the four elements.In the post-knowledge service era,the transformation of libraries not only needs to solve problems in internal and external principles,but also problems in concepts,key elements and path.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

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.