Discovering non-associated pressure-sensitive plasticity models with EUCLID
Abstract We extend (EUCLID Efficient Unsupervised Constitutive Law Identification and Discovery)—a data-driven framework for automated material model discovery—to pressure-sensitive plasticity models, encompassing arbitrarily shaped yield surfaces with convexity constraints and non-associated flow r...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Advanced Modeling and Simulation in Engineering Sciences |
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| Online Access: | https://doi.org/10.1186/s40323-024-00281-3 |
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| author | Haotian Xu Moritz Flaschel Laura De Lorenzis |
| author_facet | Haotian Xu Moritz Flaschel Laura De Lorenzis |
| author_sort | Haotian Xu |
| collection | DOAJ |
| description | Abstract We extend (EUCLID Efficient Unsupervised Constitutive Law Identification and Discovery)—a data-driven framework for automated material model discovery—to pressure-sensitive plasticity models, encompassing arbitrarily shaped yield surfaces with convexity constraints and non-associated flow rules. The method only requires full-field displacement and boundary force data from one single experiment and delivers constitutive laws as interpretable mathematical expressions. We construct a material model library for pressure-sensitive plasticity models with non-associated flow rules in four steps: (1) a Fourier series describes an arbitrary yield surface shape in the deviatoric stress plane; (2) a pressure-sensitive term in the yield function defines the shape of the shear failure surface and determines plastic deformation under tension; (3) a compression cap term determines plastic deformation under compression; (4) a non-associated flow rule may be adopted to avoid the excessive dilatancy induced by plastic deformations. In contrast to traditional parameter identification methods, EUCLID is equipped with a sparsity promoting regularization to restrain the number of model parameters (and thus modeling features) to the minimum needed to accurately interpret the data, thus achieving a compromise between model simplicity and accuracy. The convexity of the learned yield surface is guaranteed by a set of constraints in the inverse optimization problem. We demonstrate the proposed approach in multiple numerical experiments with noisy data, and show the ability of EUCLID to accurately select a suitable material model from the starting library. |
| format | Article |
| id | doaj-art-68c4d60676c448a0b4b78b574977ba53 |
| institution | Kabale University |
| issn | 2213-7467 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Advanced Modeling and Simulation in Engineering Sciences |
| spelling | doaj-art-68c4d60676c448a0b4b78b574977ba532025-01-19T12:32:56ZengSpringerOpenAdvanced Modeling and Simulation in Engineering Sciences2213-74672025-01-0112114110.1186/s40323-024-00281-3Discovering non-associated pressure-sensitive plasticity models with EUCLIDHaotian Xu0Moritz Flaschel1Laura De Lorenzis2Empa, Swiss Federal Laboratories for Material Science and TechnologyDepartment of Mechanical and Process Engineering, Institute for Mechanical Systems, ETH ZürichDepartment of Mechanical and Process Engineering, Institute for Mechanical Systems, ETH ZürichAbstract We extend (EUCLID Efficient Unsupervised Constitutive Law Identification and Discovery)—a data-driven framework for automated material model discovery—to pressure-sensitive plasticity models, encompassing arbitrarily shaped yield surfaces with convexity constraints and non-associated flow rules. The method only requires full-field displacement and boundary force data from one single experiment and delivers constitutive laws as interpretable mathematical expressions. We construct a material model library for pressure-sensitive plasticity models with non-associated flow rules in four steps: (1) a Fourier series describes an arbitrary yield surface shape in the deviatoric stress plane; (2) a pressure-sensitive term in the yield function defines the shape of the shear failure surface and determines plastic deformation under tension; (3) a compression cap term determines plastic deformation under compression; (4) a non-associated flow rule may be adopted to avoid the excessive dilatancy induced by plastic deformations. In contrast to traditional parameter identification methods, EUCLID is equipped with a sparsity promoting regularization to restrain the number of model parameters (and thus modeling features) to the minimum needed to accurately interpret the data, thus achieving a compromise between model simplicity and accuracy. The convexity of the learned yield surface is guaranteed by a set of constraints in the inverse optimization problem. We demonstrate the proposed approach in multiple numerical experiments with noisy data, and show the ability of EUCLID to accurately select a suitable material model from the starting library.https://doi.org/10.1186/s40323-024-00281-3Full-field dataModel discoverySparse regressionPressure-sensitive plasticityNon-associated flow rule |
| spellingShingle | Haotian Xu Moritz Flaschel Laura De Lorenzis Discovering non-associated pressure-sensitive plasticity models with EUCLID Advanced Modeling and Simulation in Engineering Sciences Full-field data Model discovery Sparse regression Pressure-sensitive plasticity Non-associated flow rule |
| title | Discovering non-associated pressure-sensitive plasticity models with EUCLID |
| title_full | Discovering non-associated pressure-sensitive plasticity models with EUCLID |
| title_fullStr | Discovering non-associated pressure-sensitive plasticity models with EUCLID |
| title_full_unstemmed | Discovering non-associated pressure-sensitive plasticity models with EUCLID |
| title_short | Discovering non-associated pressure-sensitive plasticity models with EUCLID |
| title_sort | discovering non associated pressure sensitive plasticity models with euclid |
| topic | Full-field data Model discovery Sparse regression Pressure-sensitive plasticity Non-associated flow rule |
| url | https://doi.org/10.1186/s40323-024-00281-3 |
| work_keys_str_mv | AT haotianxu discoveringnonassociatedpressuresensitiveplasticitymodelswitheuclid AT moritzflaschel discoveringnonassociatedpressuresensitiveplasticitymodelswitheuclid AT lauradelorenzis discoveringnonassociatedpressuresensitiveplasticitymodelswitheuclid |