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.

Architectural survey of historical buildings:The orders of classical architecture in the Baptistery of Florence

The survey of historic buildings is very important for architectural conservation.An integral survey allows perpetuating the building historical memory,deciphering it for future generations,and recovering it in case of accidental loss.The Baptistery of Florence survey was done with four measuring methods:the"direct"one,which employs traditional measuring tools;the"indirect"one,which expedites the dimension-gathering process with more preci-sion;and the"photogrammetric"one,which uses snapshots to facilitate the representation process with computers.An innova tive measuring concept,known here as the"recovery sur-vey",synthetizes graphical reality so that irregularities--due to the project materialization and deterioration over time-disappear;therefore,retrieving the building original design.Col-umns with their bases,shafts and capitals,as well as entablatures with their architraves,friezes and cornices were under study.The three Greek orders(Doric,lonic and Corinthian)together with the two roman orders(Tuscan and Composite)unveiled the classical architecture significance.These constituents,which reoccur inside and outside the baptistery,were measured as part of these holistic survey and recovery process,to achieve the objective of this research study:the recording of this world historical building through an integral survey and rilievo to decode its significance and symbolism.