Výsledky vyhledávání - "International journal for numerical methods in engineering"

A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling Autor As'ad, Faisal, Avery, Philip, Farhat, Charbel

“…A mechanics‐informed artificial neural network approach for learning constitutive laws governing complex, nonlinear, elastic materials from strain–stress data…”
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Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models Autor Kapteyn, M.G., Knezevic, D.J., Huynh, D.B.P., Tran, M., Willcox, K.E.

“…Summary This work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create…”
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Meshless physics‐informed deep learning method for three‐dimensional solid mechanics Autor Abueidda, Diab W., Lu, Qiyue, Koric, Seid

“…Deep learning (DL) and the collocation method are merged and used to solve partial differential equations (PDEs) describing structures' deformation. We have…”
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Dual-horizon peridynamics Autor Ren, Huilong, Zhuang, Xiaoying, Cai, Yongchang, Rabczuk, Timon

“…Summary In this paper, we develop a dual‐horizon peridynamics (DH‐PD) formulation that naturally includes varying horizon sizes and completely solves the…”
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CutFEM: Discretizing geometry and partial differential equations Autor Burman, Erik, Claus, Susanne, Hansbo, Peter, Larson, Mats G., Massing, André

“…Summary We discuss recent advances on robust unfitted finite element methods on cut meshes. These methods are designed to facilitate computations on complex…”
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Homogenization‐based topology optimization for high‐resolution manufacturable microstructures Autor Groen, Jeroen P., Sigmund, Ole

“…Summary This paper presents a projection method to obtain high‐resolution, manufacturable structures from efficient and coarse‐scale homogenization‐based…”
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M‐PINN: A mesh‐based physics‐informed neural network for linear elastic problems in solid mechanics Autor Wang, Lu, Liu, Guangyan, Wang, Guanglun, Zhang, Kai

“…Physics‐informed neural networks (PINNs) have emerged as a promising approach for solving a wide range of numerical problems. Nevertheless, conventional PINNs…”
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Data‐driven computing in dynamics Autor Kirchdoerfer, T., Ortiz, M.

“…Summary We formulate extensions to data‐riven computing for both distance‐minimizing and entropy‐maximizing schemes to incorporate time integration. Previous…”
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Global cracking elements: A novel tool for Galerkin‐based approaches simulating quasi‐brittle fracture Autor Zhang, Yiming, Mang, Herbert A.

“…Summary Following the so‐called cracking elements method (CEM), we propose a novel Galerkin‐based numerical approach for simulating quasi‐brittle fracture,…”
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Computational homogenization of nonlinear elastic materials using neural networks Autor Le, B. A., Yvonnet, J., He, Q.-C.

“…Summary In this work, a decoupled computational homogenization method for nonlinear elastic materials is proposed using neural networks. In this method, the…”
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On sparse regression, Lp‐regularization, and automated model discovery Autor McCulloch, Jeremy A., St. Pierre, Skyler R., Linka, Kevin, Kuhl, Ellen

“…Sparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive…”
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Undercut and overhang angle control in topology optimization: A density gradient based integral approach Autor Qian, Xiaoping

“…Summary We present an approach for controlling the undercut and the minimal overhang angle in density based topology optimization, which are useful for…”
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Mixed formulation of physics‐informed neural networks for thermo‐mechanically coupled systems and heterogeneous domains Autor Harandi, Ali, Moeineddin, Ahmad, Kaliske, Michael, Reese, Stefanie, Rezaei, Shahed

“…Physics‐informed neural networks (PINNs) are a new tool for solving boundary value problems by defining loss functions of neural networks based on governing…”
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A Stochastic Simplex Approximate Gradient (StoSAG) for optimization under uncertainty Autor Fonseca, Rahul Rahul‐Mark, Chen, Bailian, Jansen, Jan Dirk, Reynolds, Albert

“…Summary We consider a technique to estimate an approximate gradient using an ensemble of randomly chosen control vectors, known as Ensemble Optimization…”
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Local versus global stress constraint strategies in topology optimization: A comparative study Autor da Silva, Gustavo Assis, Aage, Niels, Beck, André Teófilo, Sigmund, Ole

“…Stress‐constrained topology optimization requires techniques for handling thousands to millions of stress constraints. This work presents a comprehensive…”
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Supremizer stabilization of POD-Galerkin approximation of parametrized steady incompressible Navier-Stokes equations Autor Ballarin, Francesco, Manzoni, Andrea, Quarteroni, Alfio, Rozza, Gianluigi

“…SummaryIn this work, we present a stable proper orthogonal decomposition–Galerkin approximation for parametrized steady incompressible Navier–Stokes equations…”
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Numerical approximations for a phase field dendritic crystal growth model based on the invariant energy quadratization approach Autor Zhao, Jia, Wang, Qi, Yang, Xiaofeng

“…Summary We present two accurate and efficient numerical schemes for a phase field dendritic crystal growth model, which is derived from the variation of a…”
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Mesh sampling and weighting for the hyperreduction of nonlinear Petrov–Galerkin reduced‐order models with local reduced‐order bases Autor Grimberg, Sebastian, Farhat, Charbel, Tezaur, Radek, Bou‐Mosleh, Charbel

“…The energy‐conserving sampling and weighting (ECSW) method is a hyper‐reduction method originally developed for accelerating the performance of Galerkin…”
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Topology optimization with multiple materials via moving morphable component (MMC) method Autor Zhang, Weisheng, Song, Junfu, Zhou, Jianhua, Du, Zongliang, Zhu, Yichao, Sun, Zhi, Guo, Xu

“…Summary With the fast development of additive manufacturing technology, topology optimization involving multiple materials has received ever increasing…”
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Isogeometric topology optimization for continuum structures using density distribution function Autor Gao, Jie, Gao, Liang, Luo, Zhen, Li, Peigen

“…Summary This paper will propose a more effective and efficient topology optimization method based on isogeometric analysis, termed as isogeometric topology…”
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