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fsm support vector machine is a tool of structural analysis that based on the idea that it is possible to represent a structure as a set of vector functions. with svm, the rest variables – including stress, displacement, modal response and more – can be calculated directly. the use of svm will reduce the cost of the engineering and improve the efficiency of the modern process of parameter estimation, and may even replace the need for simulations. the study of the maximum likelihood projection of the measured values and numerical optimal parameters is still a hot topic in structural mechanics as well as the svm.

machine learning has become an important tool for computer vision applications. the huge strides of computer vision applications in recent years are largely due to the development of so-called automatic machine learning methods. in this paper, we study automatic machine learning methods for structural analysis. in particular, we investigate the use of support vector machines for inverse element stiffness estimation. we demonstrate on three structural benchmarks that this approach is competitive with the conventional kriging approach and is more robust against overfitting. besides finite element analyses, this framework can be extended to other applications, such as inference of material parameters for structural health monitoring applications.

we present a simulation methodology based on hybrid graphical modeling, which can be used to efficiently model and analyze the uncertainty and variability of a large number of states. the idea is to compose a graphical model for each state by composing a graphical model for the error of the state, which is then superimposed over a known graphical model for a reference state. the model is then evolved in time, allowing us to “reject” the state that is outside the uncertainty of the reference. we illustrate our approach using two case studies and validate it by comparing the results to conventional methods (global sensitivity analysis and sampling-based uncertainty quantification). the efficiency of this method is demonstrated by experiments on a gpu cluster.

at the heart of a simulation is the mesh. if the mesh is poor, the simulation will not perform well. meshes can be generated from a number of sources, including cad models and tools, engineering or scientific data, and even from scratch. a few software tools offer the ability to build the mesh automatically. msc marc’s built-in mesh generators are extremely flexible and robust. they are designed to generate meshes from cad models, have the capability of generating meshes with elements that are not continuous or have sharp features, and automatically remove small elements as the mesh evolves. they also support the generation of meshes from engineering or scientific data, such as bézier and nurbs surfaces, and from scratch.
meshes can be analyzed to estimate and predict material properties. meshing may be the most important aspect of a simulation. msc marc’s built-in mesh analysis is extremely comprehensive. the analysis includes both traditional and modern analysis tools such as strain, stress, displacement, and strain energy. msc marc’s core feature is its ability to build up the mesh as the simulation is being performed. a few other commercial programs have mesh-building capabilities. however, msc marc’s meshing features are unmatched. it can build meshes from cad geometry, engineering or scientific data, and from scratch. it has mesh generators for building meshes of most types, including 3d solid, shell, and engineering surfaces.
simulations can be run with different boundary conditions, including temperature, velocity, strain, and stress. msc marc supports boundary conditions of many types, including temperature, velocity, pressure, and stress. msc marc’s boundary conditions are included with the analysis and simulation, so the user can modify the conditions as needed. msc marc’s boundary conditions are also very easy to use, and the user can quickly generate any type of boundary condition that is needed.
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Free Download =LINK= Marc Mentat Software

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