Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solids
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 c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2020-01-01
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| Series: | Data-Centric Engineering |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2632673620000209/type/journal_article |
| _version_ | 1811156482940469248 |
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| author | Xiaolong He Qizhi He Jiun-Shyan Chen Usha Sinha Shantanu Sinha |
| author_facet | Xiaolong He Qizhi He Jiun-Shyan Chen Usha Sinha Shantanu Sinha |
| author_sort | Xiaolong He |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T04:51:18Z |
| format | Article |
| id | doaj.art-3bfde7d58a304db0ba4a37513bf90a70 |
| institution | Directory Open Access Journal |
| issn | 2632-6736 |
| language | English |
| last_indexed | 2024-04-10T04:51:18Z |
| publishDate | 2020-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data-Centric Engineering |
| spelling | doaj.art-3bfde7d58a304db0ba4a37513bf90a702023-03-09T12:31:42ZengCambridge University PressData-Centric Engineering2632-67362020-01-01110.1017/dce.2020.20Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solidsXiaolong He0Qizhi He1https://orcid.org/0000-0002-6586-8308Jiun-Shyan Chen2https://orcid.org/0000-0002-6871-8815Usha Sinha3Shantanu Sinha4Department of Structural Engineering, University of California San Diego, La Jolla, CA 92093, USAPhysical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99354, USADepartment of Structural Engineering, University of California San Diego, La Jolla, CA 92093, USADepartment of Physics, San Diego State University, San Diego, CA 92182, USADepartment of Radiology, University of California San Diego, La Jolla, CA 92093, USAAs 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.https://www.cambridge.org/core/product/identifier/S2632673620000209/type/journal_articleAnisotropyconvexity-preserving reconstructiondata-driven computational mechanicsmanifold learningreproducing kernel (RK) approximation |
| spellingShingle | Xiaolong He Qizhi He Jiun-Shyan Chen Usha Sinha Shantanu Sinha Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solids Data-Centric Engineering Anisotropy convexity-preserving reconstruction data-driven computational mechanics manifold learning reproducing kernel (RK) approximation |
| title | Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solids |
| title_full | Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solids |
| title_fullStr | Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solids |
| title_full_unstemmed | Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solids |
| title_short | Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solids |
| title_sort | physics constrained local convexity data driven modeling of anisotropic nonlinear elastic solids |
| topic | Anisotropy convexity-preserving reconstruction data-driven computational mechanics manifold learning reproducing kernel (RK) approximation |
| url | https://www.cambridge.org/core/product/identifier/S2632673620000209/type/journal_article |
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