XI International Conference on Adaptive Modeling and Simulation最新文献_第2页

On the Discretization of the Continuous Adjoint to the Euler Equations in Aerodynamic Shape Optimization 气动形状优化中欧拉方程连续伴随的离散化

XI International Conference on Adaptive Modeling and Simulation Pub Date : 1900-01-01 DOI: 10.23967/admos.2023.056

M. Kontou, X. Trompoukis, V. Asouti, K. Giannakoglou

引用次数: 0

Digital Volume Correlation techniques for patient-specific simulation of vertebrae with metastasis 数字体积相关技术用于患者特异性模拟转移椎体

XI International Conference on Adaptive Modeling and Simulation Pub Date : 1900-01-01 DOI: 10.23967/admos.2023.035

L. Person, F. Hild, E. Nadal, J. Roderas, O. Allix

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Adaptive multilevel Monte Carlo for risk averse optimization 风险规避优化的自适应多层蒙特卡罗算法

XI International Conference on Adaptive Modeling and Simulation Pub Date : 1900-01-01 DOI: 10.23967/admos.2023.081

F. Nobile

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A Multiscale Method with Continuous Matter Addition in DED Additive Manufacturing Processes DED增材制造过程中连续物质添加的多尺度方法

XI International Conference on Adaptive Modeling and Simulation Pub Date : 1900-01-01 DOI: 10.23967/admos.2023.053

M. Picos, Q. Quintela, J. Rodríguez, P. Barral

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A Reduced Order Approximation for Identification of Non-linear Material Parameters using Optimal Control Method 用最优控制方法辨识非线性材料参数的降阶逼近

XI International Conference on Adaptive Modeling and Simulation Pub Date : 1900-01-01 DOI: 10.23967/admos.2023.024

M. Bhattacharyya, P. Feissel

{"title":"A Reduced Order Approximation for Identification of Non-linear Material Parameters using Optimal Control Method","authors":"M. Bhattacharyya, P. Feissel","doi":"10.23967/admos.2023.024","DOIUrl":"https://doi.org/10.23967/admos.2023.024","url":null,"abstract":"The objective of this research is to introduce a parametric identiﬁcation strategy based on full ﬁeld measurements obtained from digital image correlation (DIC) [1]. The optimal control method consists of segregating the equations pertaining to the modelling of the experiments into reliable and less reliable sets, and it does not require complete information of the boundaries, and measurement zone does not need to be on the complete structure. The proposed scheme is an extension of the optimal control method previously developed for determining elastic parameters [2], and herein the focus is on material parameters concerning non-linear behaviour like plasticity, damage and hardening. The optimal control approach which can be seen as a variant of the modiﬁed constitutive relation error (MCRE) method, considers the equivalence of the kinematic measurements and the model displacements to be the only unreliable equation. MCRE methods have been used previously for generalised standard materials where the constitutive behaviour can be expressed in terms of state laws and evolutions equations [3]. The resolution of the non-linear optimisation functional under non-linear constraint is achieved through an iterative solver such as the large time increment (LATIN) method. This method segregates the diﬃculty into a global linear set of equations and a non-linear local set of equations, and a space-time resolution is achieved through iterations between these two sets. Although usage of LATIN type iterative procedure in MCRE type method is not unprecedented [4], the usage of proper generalised decomposition (PGD) based reduced order approximation can be considered to be a novelty of this research. For plasticity behaviour, the quantities of interests are represented in separable variable forms (in space and","PeriodicalId":414984,"journal":{"name":"XI International Conference on Adaptive Modeling and Simulation","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124628134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Massively Parallel Simulation and Adaptive Mesh Refinement for 3D Elastostatics Contact Mechanics Problems 三维弹性接触力学问题的大规模并行仿真与自适应网格细化

XI International Conference on Adaptive Modeling and Simulation Pub Date : 1900-01-01 DOI: 10.23967/admos.2023.061

A. Epalle, I. Ramière, G. Latu, F. Lebon

{"title":"Massively Parallel Simulation and Adaptive Mesh Refinement for 3D Elastostatics Contact Mechanics Problems","authors":"A. Epalle, I. Ramière, G. Latu, F. Lebon","doi":"10.23967/admos.2023.061","DOIUrl":"https://doi.org/10.23967/admos.2023.061","url":null,"abstract":"The numerical simulation of contact mechanics problems is computationally challenging, as these problems are locally highly non-linear and non-regular. Efficient numerical solutions of such problems usually rely on adaptive mesh refinement (AMR). Even if efficient parallelizations of standard AMR techniques as h-adaptive methods begin to appear [1], their combination with contact mechanics problems remains a challenging task. Indeed, current developments on algorithms for contact mechanics problems are focusing either on non-parallelized new adaptive mesh refinement methods [2] or on parallelization methods for uniform refinement meshes [3,4]. The purpose of this work is to introduce a High Performance Computing strategy for solving 3D contact elastostatics problems with AMR on hexahedral elements. The contact is treated by a node-to-node algorithm with a penalization technique in order to deal with primal variables only. Therefore, this algorithm presents the advantages of well modelling the studied phenomenon while not increasing the number of unknowns and not modifying the formulation in an intrusive manner. Concerning the AMR strategy, we rely on a non-conforming h-adaptive refinement solution. This method has already shown to be well scalable [1,7]. Regarding the detection of the refinement zones, a Zienkiewicz-Zhu (ZZ) type error estimator is used to select the elements to be refined through a local detection criterion [5]. In addition, a geometric-based stopping criterion is applied in order to automatically stop the refinement process, even in case of local singularities. This combined strategy has recently proven its efficiency [6]. In this contribution, we endeavor to extend the combination of these contact mechanics and AMR strategies to a parallel framework. In order to carry","PeriodicalId":414984,"journal":{"name":"XI International Conference on Adaptive Modeling and Simulation","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131354620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SuperAdjoint: Super-Resolution Neural Networks in Adjoint-based Output Error Estimation 超伴随:基于伴随输出误差估计的超分辨率神经网络

XI International Conference on Adaptive Modeling and Simulation Pub Date : 1900-01-01 DOI: 10.23967/admos.2023.058

T. Hunter, S. Hulsoff, A. Sitaram

引用次数: 1

A Piggyback-Style Algorithm for Learning Improved Shearlets and TGV Discretizations 一种学习改进shearlet和TGV离散化的背驮式算法

XI International Conference on Adaptive Modeling and Simulation Pub Date : 1900-01-01 DOI: 10.23967/admos.2023.013

L. Bogensperger, A. Chambolle, T. Pock

引用次数: 0

Accelerated Simulation via Combination of Model Reduction, Surrogate Modeling and Reuse of Simulation Data 基于模型约简、代理建模和仿真数据重用的加速仿真

XI International Conference on Adaptive Modeling and Simulation Pub Date : 1900-01-01 DOI: 10.23967/admos.2023.003

A. Strauß, J. Kneifl, J. Fehr, M. Bischoff

{"title":"Accelerated Simulation via Combination of Model Reduction, Surrogate Modeling and Reuse of Simulation Data","authors":"A. Strauß, J. Kneifl, J. Fehr, M. Bischoff","doi":"10.23967/admos.2023.003","DOIUrl":"https://doi.org/10.23967/admos.2023.003","url":null,"abstract":"In many applications in Computer Aided Engineering, like parametric studies, structural optimization or virtual material design, a large number of almost similar models have to be simulated. Although the individual scenarios may diﬀer only marginally in both space and time, the same eﬀort is invested for every single new simulation with no account for experience and knowledge from previous simulations. Therefore, we have developed a method that combines Model Order Reduction (MOR), surrogate modeling and the reuse of simulation data, thus exploiting knowledge from previous simulation runs to accelerate computations in multi-query contexts. MOR allows reducing model ﬁdelity in space and time without signiﬁcantly deteriorating accuracy. By reusing simulation data, a predictor or preconditioner can be obtained from a learned surrogate model to be used in subsequent simulations. The eﬃciency of the method is showcased by the exact computation of critical points encountered in nonlinear structural analysis, such as limit and bifurcation points, by the method of extended systems [1] for systems that depend on a set of design parameters, like material or geometric properties. Such critical points are of utmost engineering signiﬁcance due to the special characteristics of the structural behavior in their vicinity. Using classical reanalysis methods, like the fold line analysis [2], the computation of critical points of almost similar systems can be accelerated. This technology is limited, however, by the fact that only small parameter variations are possible. Otherwise, the algorithm may not converge to the correct solution or fail to converge. The newly developed data-based “reduced model reanalysis” method overcomes","PeriodicalId":414984,"journal":{"name":"XI International Conference on Adaptive Modeling and Simulation","volume":"7 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132880875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Segmentation of Inhomogeneous Noisy Images via a Bayesian Model Coupled with Anisotropic Mesh Adaptation 基于贝叶斯模型和各向异性网格自适应的非均匀噪声图像分割

XI International Conference on Adaptive Modeling and Simulation Pub Date : 1900-01-01 DOI: 10.23967/admos.2023.014

M. Giacomini, S. Perotto

{"title":"Segmentation of Inhomogeneous Noisy Images via a Bayesian Model Coupled with Anisotropic Mesh Adaptation","authors":"M. Giacomini, S. Perotto","doi":"10.23967/admos.2023.014","DOIUrl":"https://doi.org/10.23967/admos.2023.014","url":null,"abstract":"Automatic image segmentation is a key process in many applications of science and engineering, from medical imaging to autonomous vehicle driving and smart agriculture monitoring. In these contexts, the presence of spatial inhomogeneities and noise challenges the robustness of segmentation strategies [1]. In this talk, a finite element-based segmentation algorithm handling images with different spatial patterns is presented. The methodology relies on a split Bregman algorithm for the minimisation of a region-based Bayesian energy functional and on an anisotropic recovery-based error estimate to drive mesh adaptation [2]. On the one hand, a Bayesian model is considered to exploit the intrinsic spatial information in inhomogeneous images [3]. To address the ill-posedness of the associated optimisation problem, a convexification technique [4] is coupled with a split Bregman algorithm for the minimisation of the regularised functional [5]. On the other hand, an anisotropic mesh adaptation procedure guarantees a smooth description of the interface between background and foreground of the image, without jagged details [2,6]. The proper alignment, sizing and shaping of the anisotropically adapted mesh elements guarantee that the increased precision is achieved with a reduced number of degrees of freedom [2,6]. Numerical experiments will be presented to showcase the performance of the resulting split-adapt Bregman algorithm on synthetic and real images featuring inhomogeneous spatial patterns. The method outperforms the standard split Bregman approach, providing accurate and robust results even in the presence of Gaussian,","PeriodicalId":414984,"journal":{"name":"XI International Conference on Adaptive Modeling and Simulation","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134614706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0