Research on the Assessment System of Computational Mechanics Courses Based on the TOPSIS Entropy Weight Model

This paper takes the assessment and evaluation of computational mechanics course as the background,and constructs a diversified course evaluation system that is student-centered and integrates both quantitative and qualitative evaluation methods.The system not only pays attention to students’practical operation and theoretical knowledge mastery but also puts special emphasis on the cultivation of students’innovative abilities.In order to realize a comprehensive and objective evaluation,the assessment and evaluation method of the entropy weight model combining TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)multi-attribute decision analysis and entropy weight theory is adopted,and its validity and practicability are verified through example analysis.This method can not only comprehensively and objectively evaluate students’learning outcomes,but also provide a scientific decision-making basis for curriculum teaching reform.The implementation of this diversified course evaluation system can better reflect the comprehensive ability of students and promote the continuous improvement of teaching quality.

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.

ODE CONVERSION TECHNIQUES AND THEIR APPLICATIONS IN COMPUTATIONAL MECHANICS

In this paper,a number of ordinary differential equation(ODE)conversion techniques for trans- formation of nonstandard ODE boundary value problems into standard forms are summarised,together with their applications to a variety of boundary value problems in computational solid mechanics,such as eigenvalue problem,geometrical and material nonlinear problem,elastic contact problem and optimal design problems through some simple and representative examples,The advantage of such approach is that various ODE bounda- ry value problems in computational mechanics can be solved effectively in a unified manner by invoking a stand- ard ODE solver.

Micro air vehicle-motivated computational biomechanics in bio-flights:aerodynamics,flight dynamics and maneuvering stability

Aiming at developing an effective tool to unveil key mechanisms in bio-flight as well as to provide guidelines for bio-inspired micro air vehicles（MAVs） design,we propose a comprehensive computational framework,which integrates aerodynamics,flight dynamics,vehicle stability and maneuverability.This framework consists of（1） a Navier-Stokes unsteady aerodynamic model;（2） a linear finite element model for structural dynamics;（3） a fluidstructure interaction（FSI） model for coupled flexible wing aerodynamics aeroelasticity;（4） a free-flying rigid body dynamic（RBD） model utilizing the Newtonian-Euler equations of 6DoF motion;and（5） flight simulator accounting for realistic wing-body morphology,flapping-wing and body kinematics,and a coupling model accounting for the nonlinear 6DoF flight dynamics and stability of insect flapping flight.Results are presented based on hovering aerodynamics with rigid and flexible wings of hawkmoth and fruitfly.The present approach can support systematic analyses of bio- and bio-inspired flight.

Editorial: Computational mechanics of granular materials

Most of granular materials are highly heteroge- neous, composed of voids and particles with different sizes and shapes. Geological matter, soil and clay in nature, geo-structure, concrete, etc. are practical ex- amples among them. From the microscopic view, a lo- cal region in the medium is occupied by particles with small but finite sizes and granular material is naturally modeled as an assembly of discrete particles in contacts On the other hand, the local region is identified with a material point in the overall structure and this discon- tinuous medium can then be represented by an effective continuum on the macroscopic level

A perspective on regression and Bayesian approaches for system identification of pattern formation dynamics

We present two approaches to system identification, i.e. the identification of partial differentialequations (PDEs) from measurement data. The first is a regression-based variational systemidentification procedure that is advantageous in not requiring repeated forward model solves andhas good scalability to large number of differential operators. However it has strict data typerequirements needing the ability to directly represent the operators through the available data.The second is a Bayesian inference framework highly valuable for providing uncertaintyquantification, and flexible for accommodating sparse and noisy data that may also be indirectquantities of interest. However, it also requires repeated forward solutions of the PDE modelswhich is expensive and hinders scalability. We provide illustrations of results on a model problemfor pattern formation dynamics, and discuss merits of the presented methods.

Data-driven computing in elasticity via kernel regression

This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary numerical experiments illustrate that, compared with existing methods, the proposed method finds a reasonable solution even if data points distribute coarsely in a given material data set.

Finite Element Analysis of Smart Structures with Piezoelectric Sensors/Actuators Including Debonding

In vibration active control of composite structures, piezoelectricsensors/actuators are usually bonded to the surface of a host structure. Debonding of piezoelectricsensors/actuators can result in significant changes to the static and dynamic response. In thepresent paper, an novel Enhanced Assumed Strain(EAS) piezoelectric solid element formulation isdeveloped for vibration active control of laminated structures bonded with piezoelectric sensors andactuators. Unlike the conventional brick elements, the present formulation is very reliable, moreaccurate, and computationally efficient and can be used to model the response of shell structuresbesides thin plates. Delaminations are modeled by pairs of nodes with the same coordinates butdifferent node numbers, and numerical results demonstrate the performance of the element and theglobal and local effects of debonding sensors/actuators on the dynamics of the adaptive laminates.

Transformation Operator and Criterion for Perfectly Teleporting Arbitrary Three-Qubit State with Six-Qubit Channel and Bell-State Measurement

Using the method presented recently [Phys.Rev.A 77（2008）014306; Phys.Lett.A 369（2007）377], the transformation operator （TO） is explicitly given for teleporting an arbitrary three-qubit state with a six-qubit channel and Bell-state measurements. A criterion on whether such quantum teleportation can be perfectly realized is educed in terms of TO. Moreover, six instantiations on TO and criterion are concisely shown.

Probabilistic Teleportation of Two-Particle State of General Formation in Ion Trap

We propose a scheme for the probabilistic teleportation of an unknown two-particle state of general formation in ion trap. It is shown that one can realize experimentally this teleportation protocol of two-particle state with presently available techniques.

Scheme for Generating Cluster States with Charge Qubits in a Cavity

Based on superconducting charge qubits （SCCQs） coupled to a single-mode microwave cavity, we propose a scheme for generating charge cluster states. For all SCCQs, the controlled gate voltages are all in their degeneracy points, the quantum information is encoded in two logic states of charge basis. The generation of the multi-qubit cluster state can be achieved step by step on a pair of nearest-neighbor qubits. Considering effective long-rang coupling, we provide an efficient way to one-step generating of a highly entangled cluster state, in which the qubit-qubit coupling is mediated by the cavity mode. Our quantum operations are insensitive to the initial state of the cavity mode by removing the influence of the cavity mode via the periodical evolution of the system. Thus, our operation may be against the decoherence from the cavity.

A computational method for the load spectra of large-scale structures with a data-driven learning algorithm

For complex engineering systems, such as trains, planes, and offshore oil platforms, load spectra are cornerstone of their safety designs and fault diagnoses. We demonstrate in this study that well-orchestrated machine learning modeling, in combination with limited experimental data, can effectively reproduce the high-fidelity, history-dependent load spectra in critical sites of complex engineering systems, such as high-speed trains. To meet the need for in-service monitoring, we propose a segmentation and randomization strategy for long-duration historical data processing to improve the accuracy of our data-driven model for longterm load-time history prediction. Results showed the existence of an optimal length of subsequence, which is associated with the characteristic dissipation time of the dynamic system. Moreover, the data-driven model exhibits an excellent generalization capability to accurately predict the load spectra for different levels of passenger-dedicated lines. In brief, we pave the way, from data preprocessing, hyperparameter selection, to learning strategy, on how to capture the nonlinear responses of such a dynamic system, which may then provide a unifying framework that could enable the synergy of computation and in-field experiments to save orders of magnitude of expenses for the load spectrum monitoring of complex engineering structures in service and prevent catastrophic fatigue and fracture in those solids.

Deformation and failure in nanomaterials via a data driven modelling approach

A data driven computational model that accounts for more than two material states has been presented in this work. Presented model can account for multiple state variables, such as stresses,strains, strain rates and failure stress, as compared to previously reported models with two states.Model is used to perform deformation and failure simulations of carbon nanotubes and carbon nanotube/epoxy nanocomposites. The model capability of capturing the strain rate dependent deformation and failure has been demonstrated through predictions against uniaxial test data taken from literature. The predicted results show a good agreement between data set taken from literature and simulations.

Physics-informed deep learning for digital materials

In this work,a physics-informed neural network(PINN)designed specifically for analyzing digital mate-rials is introduced.This proposed machine learning(ML)model can be trained free of ground truth data by adopting the minimum energy criteria as its loss function.Results show that our energy-based PINN reaches similar accuracy as supervised ML models.Adding a hinge loss on the Jacobian can constrain the model to avoid erroneous deformation gradient caused by the nonlinear logarithmic strain.Lastly,we discuss how the strain energy of each material element at each numerical integration point can be calculated parallelly on a GPU.The algorithm is tested on different mesh densities to evaluate its com-putational efficiency which scales linearly with respect to the number of nodes in the system.This work provides a foundation for encoding physical behaviors of digital materials directly into neural networks,enabling label-free learning for the design of next-generation composites.

MIXED COMPATIBLE ELEMENT AND MIXED HYBRID INCOMPATIBLE ELEMENT VARIATIONAL METHODS IN DYNAMICS OF VISCOUS BAROTROPIC FLUIDS

This paper presents and proves the mixed compatible finite element variationalprinciples in dynamics of viscous barotropic fluids. When the principles are proved, itis found that the compatibility conditions of stress can be naturally satisfied. The gene-rallzed variational principles with mixed hybrid incompatible finite elements are alsopresented and proved, and they can reduce the computation of incompatible elements indynamics of viscous barotropic flows.

Analytical-Numerical Modeling Approach for Calculation of the Structural Distortions after Welding and Thermal Straightening

The analytic-numerical hybrid model for calculating welding distortions in large welded structures is presented. Objective of the analytical model is the calculation of the plastic strains and their distribution after welding and thermal straightening process. The consideration of the essential physical relations is put into discussion. Afterwards the obtained plastic strains by the analytical calculation are loaded on an elastic FE-model of the structure and the distortions of the whole structure are predicted. The consideration of welding and thermal straightening scenarios and the assembling stages is done by taking into account the intermediate variation of the strain state at every processing step. The model is intended to be used for solving industrial tasks, i.e. intending acceptable precision and calculation time as well as low simulation costs. The application of the model is demonstrated on structures with many welds and straightening spots.

Correlations Existing in Three-Qubit Product States

Analytic results of the relationship between local noncommutativity and non-violations of Svetlichny inequalities for three-qubit separable states are obtained. It is shown that the converse trade-off relations presented by Seevinck and Uffinck [Phys. Rev. A 2007 76 042105] do not always hold for three-qubit states, and that there exists some correlation even though the state is the simple product state.

Control of a Cloud of Laser-Cooled ^40Ca^＋ in a Linear Ion Trap

A cloud of laser-cooled ^40Ca^＋ is successfully trapped and manipulated under well control in our home-built linear ion trap, which is designed and constructed solely for studying quantum information processing. By exploring the variation of the ion cloud with respect to the trap parameters, we have optimized the trapping condition and obtained very good fluorescence spectra. We observe the dynamics of the ion cloud, and estimate the temperature of the ion cloud to be of the order of milli-Kelvin.

Security Analysis of a Block Encryption Algorithm Based on Dynamic Sequences of Multiple Chaotic Systems

Recently, the cryptosystem based on chaos has attracted much attention. Wang and Yu （Commun. Nonlin. Sci. Numer. Simulat. 14（2009）574） proposed a block encryption algorithm based on dynamic sequences of multiple chaotic systems. We analyze the potential flaws in the algorithm. Then, a chosen-plaintext attack is presented. Some remedial measures are suggested to avoid the flaws effectively. Furthermore, an improved encryption algorithm is proposed to resist the attacks＂ and to keep all the merits of the original cryptosystem.

Cryptanalysis of an Improved Flexible Ping-Pong Protocol in Perfect and Imperfect Quantum Channels

We recently proposed a flexible quantum secure direct communication protocol [Chin. Phys. Lett. 23 （2006） 3152]. By analyzing its security in the perfect channel from the aspect of quantum information theory, we find that an eavesdropper is capable of stealing all the information without being detected. Two typical attacks are presented to illustrate this point. A solution to this loophole is also suggested and we show its powerfulness against the most general individual attack in the ideal case. We also discuss the security in the imperfect case when there is noise and loss.