Journal article 105 views 11 downloads
A generalized theory for physics-augmented neural networks in finite strain thermo-electro-mechanics
,
J. Martínez-Frutos ,
A. Pérez-Escolar,
I. Castañar ,
Niklas Hellmer,
Antonio Gil
Computer Methods in Applied Mechanics and Engineering, Volume: 437, Start page: 117741
Swansea University Authors:
Niklas Hellmer, Antonio Gil
-
PDF | Accepted Manuscript
Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention).
Download (36.49MB)
DOI (Published version): 10.1016/j.cma.2025.117741
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 |
|---|---|
| ISSN: | 0045-7825 |
| Published: |
Elsevier BV
2025
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa68679 |
| 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. |
|---|---|
| Keywords: |
Neural networks; Machine learning; Thermo-electro-mechanics; Dielectric elastomers; Finite elements |
| College: |
Faculty of Science and Engineering |
| Funders: |
R. Ortigosa, J. Martínez-Frutos and I. Castañar acknowledge the support of grant PID2022-141957OA-C22 funded by MICIU/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. R. Ortigosa and J. Martínez-Frutos also acknowledge the support provided by the Autonomous Community of the Region of Murcia, Spain through the programme for the development of scientific and technical research by competitive groups (21996/PI/22), included in the Regional Program for the Promotion of Scientific and Technical Research of Fundacion Seneca - Agencia de Ciencia
Tecnologia de la Region de Murcia. N. Ellmer and A. J. Gil acknowledge the support provided by the defence, science and technology laboratory (Dstl), United Kingdom. A. J. Gil acknowledges the financial support provided by the Leverhulme Trust, United Kingdom . |
| Start Page: |
117741 |