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
The authors regret“The inaccurate author’s affiliation spelling and labeling errors.a National Key Laboratory for the Modernization of Classical and Famous Prescriptions of Chinese Medicine,No.788 Huoju Avenue,Gaoxi...The authors regret“The inaccurate author’s affiliation spelling and labeling errors.a National Key Laboratory for the Modernization of Classical and Famous Prescriptions of Chinese Medicine,No.788 Huoju Avenue,Gaoxin Dev District,Nanchang 330096,PR China.The correct author’s affiliation spelling and labeling is as below.a State Key Laboratory for the Modernization of Classical and Famous Prescriptions of Chinese Medicine,No.788 Huoju Avenue,Gaoxin Dev District,Nanchang 330096,PR China.”展开更多
All movies in Chinese with English subtitles.FREE ENTRY.MOVIE VENUES Beijing Jianghu Jiuba江湖酒吧7 Dongmianhua Hutong,Jiaodaokou Nan Dajie交道口南大街东棉花胡同7号(10)6401-4611Doors open at 7:00pm Movies start at 7:30pm
The authors regret“The inaccurate author’s affiliation spelling and labeling errors.”a National Key Laboratory for the Modernization of Classical and Famous Prescriptions of Chinese Medicine,No.788 Huoju Avenue,Gao...The authors regret“The inaccurate author’s affiliation spelling and labeling errors.”a National Key Laboratory for the Modernization of Classical and Famous Prescriptions of Chinese Medicine,No.788 Huoju Avenue,Gaoxin Dev District,Nanchang 330096,PR China.展开更多
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.
文摘The authors regret“The inaccurate author’s affiliation spelling and labeling errors.a National Key Laboratory for the Modernization of Classical and Famous Prescriptions of Chinese Medicine,No.788 Huoju Avenue,Gaoxin Dev District,Nanchang 330096,PR China.The correct author’s affiliation spelling and labeling is as below.a State Key Laboratory for the Modernization of Classical and Famous Prescriptions of Chinese Medicine,No.788 Huoju Avenue,Gaoxin Dev District,Nanchang 330096,PR China.”
文摘All movies in Chinese with English subtitles.FREE ENTRY.MOVIE VENUES Beijing Jianghu Jiuba江湖酒吧7 Dongmianhua Hutong,Jiaodaokou Nan Dajie交道口南大街东棉花胡同7号(10)6401-4611Doors open at 7:00pm Movies start at 7:30pm
文摘The authors regret“The inaccurate author’s affiliation spelling and labeling errors.”a National Key Laboratory for the Modernization of Classical and Famous Prescriptions of Chinese Medicine,No.788 Huoju Avenue,Gaoxin Dev District,Nanchang 330096,PR China.
