Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solids

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiaolong He, Qizhi He, Jiun-Shyan Chen, U. Sinha, S. Sinha
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引用次数: 10

Abstract

Abstract As characterization and modeling of complex materials by phenomenological models remains challenging, data-driven computing that performs physical simulations directly from material data has attracted considerable attention. Data-driven computing is a general computational mechanics framework that consists of a physical solver and a material solver, based on which data-driven solutions are obtained through minimization procedures. This work develops a new material solver built upon the local convexity-preserving reconstruction scheme by He and Chen (2020) A physics-constrained data-driven approach based on locally convex reconstruction for noisy database. Computer Methods in Applied Mechanics and Engineering 363, 112791 to model anisotropic nonlinear elastic solids. In this approach, a two-level local data search algorithm for material anisotropy is introduced into the material solver in online data-driven computing. A material anisotropic state characterizing the underlying material orientation is used for the manifold learning projection in the material solver. The performance of the proposed data-driven framework with noiseless and noisy material data is validated by solving two benchmark problems with synthetic material data. The data-driven solutions are compared with the constitutive model-based reference solutions to demonstrate the effectiveness of the proposed methods.
各向异性非线性弹性固体的物理约束局部凸性数据驱动建模
由于利用现象学模型对复杂材料进行表征和建模仍然具有挑战性,直接从材料数据进行物理模拟的数据驱动计算引起了相当大的关注。数据驱动计算是由物理求解器和材料求解器组成的通用计算力学框架,在此基础上通过最小化程序获得数据驱动解。本工作基于He和Chen(2020)的局部凸保持重构方案开发了一种新的材料求解器。计算机方法在各向异性非线性弹性固体模型中的应用[j] .力学与工程学报,36(2):771 - 771。该方法将材料各向异性的两级局部数据搜索算法引入到在线数据驱动计算中的材料求解器中。材料求解器中的流形学习投影采用表征底层材料取向的材料各向异性状态。通过求解合成材料数据的两个基准问题,验证了该数据驱动框架在无噪声和有噪声材料数据下的性能。将数据驱动解与基于本构模型的参考解进行了比较,验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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