Journal article 9 views
A generalised theory for physics-augmented neurals networks in finite strain thermo-electro-mechanics
R. Ortigosa,
J. Martınez-Frutos,
A. Perez-Escolar,
I. Castanar,
Niklas Hellmer,
Antonio Gil 
Computer Methods in Applied Mechanics and Engineering
Swansea University Authors:
Niklas Hellmer, Antonio Gil
Abstract
This manuscript introduces a novel neural network-based computational framework for constistutive modelling of thermo-electro-mechanically coupled materials at finite strains, with four key innovations: (i) It supports calibration of neural network models with various input forms, such as Ψnn(F, E0,...
| Published in: | Computer Methods in Applied Mechanics and Engineering |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa68679 |
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2025-01-13T20:35:09Z |
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2025-01-13T20:35:09Z |
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cronfa68679 |
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SURis |
| fullrecord |
<?xml version="1.0"?><rfc1807><datestamp>2025-01-13T12:38:46.4579236</datestamp><bib-version>v2</bib-version><id>68679</id><entry>2025-01-13</entry><title>A generalised theory for physics-augmented neurals networks in finite strain thermo-electro-mechanics</title><swanseaauthors><author><sid>a4ee2ab85ef1dbcd71b56b2dab40f6bb</sid><firstname>Niklas</firstname><surname>Hellmer</surname><name>Niklas Hellmer</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>1f5666865d1c6de9469f8b7d0d6d30e2</sid><ORCID>0000-0001-7753-1414</ORCID><firstname>Antonio</firstname><surname>Gil</surname><name>Antonio Gil</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2025-01-13</date><deptcode>MACS</deptcode><abstract>This manuscript introduces a novel neural network-based computational framework for constistutive modelling of thermo-electro-mechanically coupled materials at finite strains, with four key innovations: (i) It supports calibration of neural network models with various input forms, such as Ψnn(F, E0, θ), enn(F, D0, η), Υnn(F, E0, η), or Γnn(F, D0, θ), with F representing the deformation gradient tensor, E0 and D0 the electric field and electric displacement field, respectively and finally, θ and η, the temperature and entropy fields. These models comply with physical laws and material symmetries by utilizing isotropic or anisotropic invariants corresponding to the material’s symmetry group. (ii) A calibration approach is developed for the case of experimental data, where entropy η is typically unmeasurable. (iii) The framework accommodates models like enn(F, D0, η), specially convenient for the imposition of polyconvexity across the three physics involved. A detailed calibration study is conducted evaluating various neural network architectures and considering a large variety of ground truth thermo-electro-mechanical constitutive models. The results demonstrate excellent predictive performance on larger datasets, validated through complex finite element simulations using both ground truth and neural network-based models. Crucially, the framework can be straightforwardly extended to scenarios involving other physics.</abstract><type>Journal Article</type><journal>Computer Methods in Applied Mechanics and Engineering</journal><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords/><publishedDay>0</publishedDay><publishedMonth>0</publishedMonth><publishedYear>0</publishedYear><publishedDate>0001-01-01</publishedDate><doi/><url/><notes/><college>COLLEGE NANME</college><department>Mathematics and Computer Science School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MACS</DepartmentCode><institution>Swansea University</institution><apcterm/><funders/><projectreference/><lastEdited>2025-01-13T12:38:46.4579236</lastEdited><Created>2025-01-13T12:32:52.5765638</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering</level></path><authors><author><firstname>R.</firstname><surname>Ortigosa</surname><order>1</order></author><author><firstname>J.</firstname><surname>Martınez-Frutos</surname><order>2</order></author><author><firstname>A.</firstname><surname>Perez-Escolar</surname><order>3</order></author><author><firstname>I.</firstname><surname>Castanar</surname><order>4</order></author><author><firstname>Niklas</firstname><surname>Hellmer</surname><order>5</order></author><author><firstname>Antonio</firstname><surname>Gil</surname><orcid>0000-0001-7753-1414</orcid><order>6</order></author></authors><documents/><OutputDurs/></rfc1807> |
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2025-01-13T12:38:46.4579236 v2 68679 2025-01-13 A generalised theory for physics-augmented neurals networks in finite strain thermo-electro-mechanics a4ee2ab85ef1dbcd71b56b2dab40f6bb Niklas Hellmer Niklas Hellmer true false 1f5666865d1c6de9469f8b7d0d6d30e2 0000-0001-7753-1414 Antonio Gil Antonio Gil true false 2025-01-13 MACS This manuscript introduces a novel neural network-based computational framework for constistutive modelling of thermo-electro-mechanically coupled materials at finite strains, with four key innovations: (i) It supports calibration of neural network models with various input forms, such as Ψnn(F, E0, θ), enn(F, D0, η), Υnn(F, E0, η), or Γnn(F, D0, θ), with F representing the deformation gradient tensor, E0 and D0 the electric field and electric displacement field, respectively and finally, θ and η, the temperature and entropy fields. These models comply with physical laws and material symmetries by utilizing isotropic or anisotropic invariants corresponding to the material’s symmetry group. (ii) A calibration approach is developed for the case of experimental data, where entropy η is typically unmeasurable. (iii) The framework accommodates models like enn(F, D0, η), specially convenient for the imposition of polyconvexity across the three physics involved. A detailed calibration study is conducted evaluating various neural network architectures and considering a large variety of ground truth thermo-electro-mechanical constitutive models. The results demonstrate excellent predictive performance on larger datasets, validated through complex finite element simulations using both ground truth and neural network-based models. Crucially, the framework can be straightforwardly extended to scenarios involving other physics. Journal Article Computer Methods in Applied Mechanics and Engineering 0 0 0 0001-01-01 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2025-01-13T12:38:46.4579236 2025-01-13T12:32:52.5765638 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering R. Ortigosa 1 J. Martınez-Frutos 2 A. Perez-Escolar 3 I. Castanar 4 Niklas Hellmer 5 Antonio Gil 0000-0001-7753-1414 6 |
| title |
A generalised theory for physics-augmented neurals networks in finite strain thermo-electro-mechanics |
| spellingShingle |
A generalised theory for physics-augmented neurals networks in finite strain thermo-electro-mechanics Niklas Hellmer Antonio Gil |
| title_short |
A generalised theory for physics-augmented neurals networks in finite strain thermo-electro-mechanics |
| title_full |
A generalised theory for physics-augmented neurals networks in finite strain thermo-electro-mechanics |
| title_fullStr |
A generalised theory for physics-augmented neurals networks in finite strain thermo-electro-mechanics |
| title_full_unstemmed |
A generalised theory for physics-augmented neurals networks in finite strain thermo-electro-mechanics |
| title_sort |
A generalised theory for physics-augmented neurals networks in finite strain thermo-electro-mechanics |
| author_id_str_mv |
a4ee2ab85ef1dbcd71b56b2dab40f6bb 1f5666865d1c6de9469f8b7d0d6d30e2 |
| author_id_fullname_str_mv |
a4ee2ab85ef1dbcd71b56b2dab40f6bb_***_Niklas Hellmer 1f5666865d1c6de9469f8b7d0d6d30e2_***_Antonio Gil |
| author |
Niklas Hellmer Antonio Gil |
| author2 |
R. Ortigosa J. Martınez-Frutos A. Perez-Escolar I. Castanar Niklas Hellmer Antonio Gil |
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Journal article |
| container_title |
Computer Methods in Applied Mechanics and Engineering |
| institution |
Swansea University |
| college_str |
Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
| department_str |
School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering |
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| description |
This manuscript introduces a novel neural network-based computational framework for constistutive modelling of thermo-electro-mechanically coupled materials at finite strains, with four key innovations: (i) It supports calibration of neural network models with various input forms, such as Ψnn(F, E0, θ), enn(F, D0, η), Υnn(F, E0, η), or Γnn(F, D0, θ), with F representing the deformation gradient tensor, E0 and D0 the electric field and electric displacement field, respectively and finally, θ and η, the temperature and entropy fields. These models comply with physical laws and material symmetries by utilizing isotropic or anisotropic invariants corresponding to the material’s symmetry group. (ii) A calibration approach is developed for the case of experimental data, where entropy η is typically unmeasurable. (iii) The framework accommodates models like enn(F, D0, η), specially convenient for the imposition of polyconvexity across the three physics involved. A detailed calibration study is conducted evaluating various neural network architectures and considering a large variety of ground truth thermo-electro-mechanical constitutive models. The results demonstrate excellent predictive performance on larger datasets, validated through complex finite element simulations using both ground truth and neural network-based models. Crucially, the framework can be straightforwardly extended to scenarios involving other physics. |
| published_date |
0001-01-01T14:39:29Z |
| _version_ |
1821326148743725056 |
| score |
11.048042 |