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This paper presents an application of evolutionary genetic techniques to the identification of internal parameters of a mass-spring physically-based animation model.
A physical model of fabrics is first presented. It uses a mass-spring mesh and an inverse dynamics procedure in order to model the non-linear elasticity of fabrics.
A method to identif...
Citations
... The first one involves the use of experimental data, often in the form of video films showing the behavior of the test sample and such an adaptation of the spring coefficients as to obtain the most accurate representation of its actual behavior during the simulation. This adaptation is obtained by iterative route often using genetic algorithms [16]. The advantage of this method is therefore a faithful representation of the tested, real object. ...
... This additional formula (16) can be used to replace the basic equation (13) to avoid division by zero when there is no strain in the direction under consideration. ...
... The first approach is based on optimization methods to minimize the difference between the results solved by the mass-spring model and the reference data. These reference data can come from the measurements, the visual appearance of real objects [34] or numerical solutions using validated methods such as finite element methods [35,36] . In general, this approach cannot be applied if the system has too many degrees of freedom with many unknown spring constants or the mesh topology varies in time since one set of parameters works for solely one mesh structure. ...
The secret to the spectacular flight capabilities of flapping insects lies in their wings, which are often approximated as flat, rigid plates. Real wings are however delicate structures, composed of veins and membranes, and can undergo significant deformation. In the present work, we present detailed numerical simulations of such deformable wings. Our results are obtained with a fluid–structure interaction solver, coupling a mass–spring model for the flexible wing with a pseudo-spectral code solving the incompressible Navier–Stokes equations. We impose the no-slip boundary condition through the volume penalization method; the time-dependent complex geometry is then completely described by a mask function. This allows solving the governing equations of the fluid on a regular Cartesian grid. Our implementation for massively parallel computers allows us to perform high resolution computations with up to 500 million grid points. The mass–spring model uses a functional approach, thus modeling the different mechanical behaviors of the veins and the membranes of the wing. We perform a series of numerical simulations of a flexible revolving bumblebee wing at a Reynolds number. In order to assess the influence of wing flexibility on the aerodynamics, we vary the elasticity parameters and study rigid, flexible and highly flexible wing models. Code validation is carried out by computing classical benchmarks.
... The first approach is based on optimization methods to minimize the difference between the results solved by the mass-spring model and the reference data. These reference data can come from the measurements, the visual appearance of real objects [34] or numerical solutions using validated methods such as finite element methods [35,36]. In general, this approach cannot be applied if the system has too many degrees of freedom with many unknown spring constants or the mesh topology varies in time since one set of parameters works for solely one mesh structure. ...
The secret to the spectacular flight capabilities of flapping insects lies in their wings, which are often approximated as flat, rigid plates. Real wings are however delicate structures, composed of veins and membranes, and can undergo significant deformation. In the present work, we present detailed numerical simulations of such deformable wings. Our results are obtained with a fluid-structure interaction solver, coupling a mass-spring model for the flexible wing with a pseudo-spectral code solving the incompressible Navier-Stokes equations. We impose the no-slip boundary condition through the volume penalization method; the time-dependent complex geometry is then completely described by a mask function. This allows solving the governing equations of the fluid on a regular Cartesian grid. Our implementation for massively parallel computers allows us to perform high resolution computations with up to 500 million grid points. The mass-spring model uses a functional approach, thus modeling the different mechanical behaviors of the veins and the membranes of the wing. We perform a series of numerical simulations of a flexible revolving bumblebee wing at a Reynolds number Re = 1800. In order to assess the influence of wing flexibility on the aerodynamics, we vary the elasticity parameters and study rigid, flexible and highly flexible wing models. Code validation is carried out by computing classical benchmarks.
... Consequently, the nonlinear stress-strain relationship of soft biological tissues is difficult to be reproduced accurately by MSM. Owing to this, optimization algorithms such as the simulated annealing (SA) [41] and Genetic algorithms (GA) [42] are often employed for optimization of spring stiffness constants by fitting the deformation of MSM to some reference data to achieve certain global mechanical behaviors. However, parameter optimization is a tedious task, and the result of a particular optimization may no longer be valid if model topology arrangement and boundary conditions are changed. ...
This paper presents a survey of the state-of-the-art deformable models studied in the literature concerning soft tissue deformable modeling for interactive surgical simulation. It first introduces the challenges of surgical simulation, followed by discussions and analyses on the deformable models, which are classified into three categories: the heuristic modeling methodology, continuum-mechanical methodology, and other methodologies. It also examines linear and nonlinear deformable modeling, model internal forces, and numerical time integrations, together with modeling of soft tissue anisotropy, viscoelasticity, and compressibility. Finally, various issues in the existing deformable models are discussed to outline the remaining challenges of deformable models in surgical simulation.
... Having such sets of the goal data it is easier to adjust the MSS accordingly and find the optimal parameters. This method was eagerly used by Louchet et al., who introduced visual comparison combined with evolutionary genetic strategy [101]. The goal was to model isotropic, homogeneous cloth, which consisted of 6 types of springs, each described by 3 parameters, but simplified to 5 parameters for the fabrics: spring stiffness, elongation rate, and 3 pairs of natural lengths. ...
The need for simulations of soft tissues, like internal organs, arises with the progress of the scientific and medical environments. The goal of my PhD is to develop a novel generic topological and physical model to simulate human organs. Such a model shall be easy to use, perform the simulations in the real time and which accuracy will allow usage for the medical purposes.This thesis explores novel simulation methods and improvement approaches for modeling deformable bodies. The methods aim at fast and robust simulations with physically accurate results. The main interest lies in simulating elastic soft tissues at small and large strains for medical purposes. We show however, that in the existing methods the accuracyto freely simulate deformable bodies and the real-time performance do not go hand in hand. Additionally, to reach the goal of simulating fast, many of the approaches move the necessary calculations to pre-computational part of the simulation, which results in inability to perform topological operations like cutting or refining.The framework used for simulations in this thesis is designed to simulate materials using Mass Spring Systems (MSS) with particular input parameters. Using Mass-Spring System, which is known for its simplicity and ability to perform fast simulations, we present several physically-based improvements to control global features of MSS which play the key role in simulation of real bodies
... Consequently, the nonlinear stress-strain relationship of soft biological tissues is difficult to be reproduced accurately by MSM. Owing to this, optimization algorithms such as the simulated annealing (SA) [41] and Genetic algorithms (GA) [42] are often employed for optimization of spring stiffness constants by fitting the deformation of MSM to some reference data to achieve certain global mechanical behaviors. However, parameter optimization is a tedious task, and the result of a particular optimization may no longer be valid if model topology arrangement and boundary conditions are changed. ...
This paper presents a survey of the state-of-the-art deformable models studied in the literature concerning soft tissue deformable modeling for interactive surgical simulation. It first introduces the challenges of surgical simulation, followed by discussions and analyses on the deformable models, which are classified into three categories: the heuristic modeling methodology, continuum-mechanical methodology, and other methodologies. It also examines linear and nonlinear deformable modeling, model internal forces, and numerical time integrations, together with modeling of soft tissue anisotropy, viscoelasticity, and compressibility. Finally, various issues in the existing deformable models are discussed to outline the remaining challenges of deformable models in surgical simulation.
... On the whole, instantiation of the stiffness values for spring-mass models is not a straightforward task. Several attempts have been reported in the literature, describing ways of finding the best stiffness parameters for spring-mass models for different applications; for the most part, they are of a heuristic nature [26,27]. We have chosen the framework of Genetic Algorithms (GAs) as a means to optimise stiffness setting of the given stent device. ...
In this paper, we perform a comparative analysis between two computational methods for virtual stent deployment: a novel fast virtual stenting method, which is based on a spring–mass model, is compared with detailed finite element analysis in a sequence of in silico experiments. Given the results of the initial comparison, we present a way to optimise the fast method by calibrating a set of parameters with the help of a genetic algorithm, which utilises the outcomes of the finite element analysis as a learning reference. As a result of the calibration phase, we were able to substantially reduce the force measure discrepancy between the two methods and validate the fast stenting method by assessing the differences in the final device configurations.
... On the whole, instantiation of the stiffness values for spring-mass models is not a straightforward task. Several attempts have been reported in the literature, describing ways of finding the best stiffness parameters for spring-mass models for different applications; for the most part, they are of a heuristic nature [26,27]. We have chosen the framework of Genetic Algorithms (GAs) as a means to optimise stiffness setting of the given stent device. ...
In this paper, we perform a comparative analysis between two computational methods for virtual stent deployment: a novel fast virtual stenting method, which is based on a spring-mass model, is compared with detailed finite element analysis in a sequence of in silico experiments. Given the results of the initial comparison, we present a way to optimise the fast method by calibrating a set of parameters with the help of a genetic algorithm, which utilises the outcomes of the finite element analysis as a learning reference. As a result of the calibration phase, we were able to substantially reduce the force measure discrepancy between the two methods and validate the fast stenting method by assessing the differences in the final device configurations.
... Evolutionary computation has already been used for estimating the behavior of soft tissue successfully [11]- [13]. Specifically the works presented in [14], [15] used an iterative search based on genetic algorithms in order to estimate the biomechanical model of the liver and the heart from two MRI images, respectively. ...
The accuracy of the patient-specific biomechanical models of the breast is a major concern for applications related with simulation, surgical guidance or cancer diagnosis. Being able to predict the localization of a lesion depends on the realism of the selected model. However, obtaining a realistic model of the breast tissues is not straightforward since the biomechanical parameters of the breast internal tissues vary significantly from one patient to another. This paper presents an iterative search algorithm which is able to obtain the parameters of a proposed biomechanical model. A methodology based on genetic algorithms was used in order to estimate the biomechanical model of the breast tissues. The similarity between the estimated model and the real model presents an overlap of about 94% and a maximum average distance below 1mm. This algorithm can be easily translated to real cases.
... They are based on the combination of a set of solutions, called population solutions, in order to create new ones. Evolutionary computation has already been proposed in the literature for parameter identification of deformable models using mass-spring models282930 and more specifically for soft tissues [31,32]. Examples of these algorithms are the scatter search (SS) and genetic algorithms (GA). ...
