Publications - Mulitphysic Simulation & Optimization Lab

19274/PI/14 21996/PI/22 Analysis Applied Mathematics Computational Mathematics Computational Mechanics Computational Theory and Mathematics Computer Graphics and Computer-Aided Design Computer Science Applications Control and Optimization Control and Systems Engineering DICOPMA Dielectric elastomer Dielectric elastomers Electro-active polymer Energy–momentum scheme Evolutionary topology optimization Fail-safe design Finite element method General Engineering General Mathematics Mechanical Engineering Nonlinear electro-elasticity Numerical Analysis Ocean Engineering PID2022-141957OA-C22 Polyconvexity Rank-one laminates Software Topology optimisation 
Show all
2025
 Pattinson, Rollo;  Ellmer, Nathan;  Hossain, Mokarram;  Ortigosa, Rogelio;  Martínez-Frutos, Jesús;  Gil, Antonio J.;  Bastola, Anil
Towards fully 3D printed dielectric elastomer actuators—A mini review Journal Article 
In: Additive Manufacturing Letters, vol. 14, pp. 100304, 2025, ISSN: 2772-3690.
Abstract | BibTeX | Tags: Additive manufacturing, Dielectric elastomer actuators, Electro-active polymers, PID2022-141957OA-C22, Topology optimisation | Links: 
Close
@article{PATTINSON2025100304,

title = {Towards fully 3D printed dielectric elastomer actuators—A mini review},

author = {Rollo Pattinson and Nathan Ellmer and Mokarram Hossain and Rogelio Ortigosa and Jesús Martínez-Frutos and Antonio J. Gil and Anil Bastola},

url = {https://www.sciencedirect.com/science/article/pii/S2772369025000374},

doi = {https://doi.org/10.1016/j.addlet.2025.100304},

issn = {2772-3690},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Additive Manufacturing Letters},

volume = {14},

pages = {100304},

abstract = {Dielectric elastomer actuators (DEAs) have attracted the interest of researchers in soft robotics and biomimetics, due to their versatile capabilities, explored through numerical analysis and experimentation. Advances in computational simulation techniques have accelerated numerical studies on DEAs, enabling even design optimisation for improved performance. However, as computational models grow in sophistication, the fabrication methods required often exceed the capabilities of traditional manufacturing. Additive manufacturing, in particular 3D printing, offers a promising solution to the challenges of realising intricate multi-functional designs developed through topology optimisation. Its precision and ability to create complex geometries make it well-suited for translating computational designs into functional DEA devices. This mini-review examines recent progress in 3D printing for DEA fabrication, emphasising its role in bridging the gap between computational design and physical devices. It also highlights emerging technologies and key challenges that must be addressed to fully realise topologically optimised DEA designs.},

keywords = {Additive manufacturing, Dielectric elastomer actuators, Electro-active polymers, PID2022-141957OA-C22, Topology optimisation},

pubstate = {published},

tppubtype = {article}

}

Close
Dielectric elastomer actuators (DEAs) have attracted the interest of researchers in soft robotics and biomimetics, due to their versatile capabilities, explored through numerical analysis and experimentation. Advances in computational simulation techniques have accelerated numerical studies on DEAs, enabling even design optimisation for improved performance. However, as computational models grow in sophistication, the fabrication methods required often exceed the capabilities of traditional manufacturing. Additive manufacturing, in particular 3D printing, offers a promising solution to the challenges of realising intricate multi-functional designs developed through topology optimisation. Its precision and ability to create complex geometries make it well-suited for translating computational designs into functional DEA devices. This mini-review examines recent progress in 3D printing for DEA fabrication, emphasising its role in bridging the gap between computational design and physical devices. It also highlights emerging technologies and key challenges that must be addressed to fully realise topologically optimised DEA designs.
Close
2022
 Klein, Dominik K.;  Ortigosa, Rogelio;  Martínez-Frutos, Jesús;  Weeger, Oliver
Finite electro-elasticity with physics-augmented neural networks Journal Article 
In: Computer Methods in Applied Mechanics and Engineering, vol. 400, pp. 115501, 2022, ISSN: 0045-7825.
Abstract | BibTeX | Tags: Constitutive modeling, Electro-active polymers, Homogenization, Nonlinear electro-elasticity, Physics-augmented machine learning | Links: 
Close
@article{KLEIN2022115501,

title = {Finite electro-elasticity with physics-augmented neural networks},

author = {Dominik K. Klein and Rogelio Ortigosa and Jesús Martínez-Frutos and Oliver Weeger},

url = {https://www.sciencedirect.com/science/article/pii/S004578252200514X},

doi = {https://doi.org/10.1016/j.cma.2022.115501},

issn = {0045-7825},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Computer Methods in Applied Mechanics and Engineering},

volume = {400},

pages = {115501},

abstract = {In the present work, a machine learning based constitutive model for electro-mechanically coupled material behavior at finite deformations is proposed. Using different sets of invariants as inputs, an internal energy density is formulated as a convex neural network. In this way, the model fulfills the polyconvexity condition which ensures material stability, as well as thermodynamic consistency, objectivity, material symmetry, and growth conditions. Depending on the considered invariants, this physics-augmented machine learning model can either be applied for compressible or nearly incompressible material behavior, as well as for arbitrary material symmetry classes. The applicability and versatility of the approach is demonstrated by calibrating it on transversely isotropic data generated with an analytical potential, as well as for the effective constitutive modeling of an analytically homogenized, transversely isotropic rank-one laminate composite and a numerically homogenized cubic metamaterial. These examinations show the excellent generalization properties that physics-augmented neural networks offer also for multi-physical material modeling such as nonlinear electro-elasticity.},

keywords = {Constitutive modeling, Electro-active polymers, Homogenization, Nonlinear electro-elasticity, Physics-augmented machine learning},

pubstate = {published},

tppubtype = {article}

}

Close
In the present work, a machine learning based constitutive model for electro-mechanically coupled material behavior at finite deformations is proposed. Using different sets of invariants as inputs, an internal energy density is formulated as a convex neural network. In this way, the model fulfills the polyconvexity condition which ensures material stability, as well as thermodynamic consistency, objectivity, material symmetry, and growth conditions. Depending on the considered invariants, this physics-augmented machine learning model can either be applied for compressible or nearly incompressible material behavior, as well as for arbitrary material symmetry classes. The applicability and versatility of the approach is demonstrated by calibrating it on transversely isotropic data generated with an analytical potential, as well as for the effective constitutive modeling of an analytically homogenized, transversely isotropic rank-one laminate composite and a numerically homogenized cubic metamaterial. These examinations show the excellent generalization properties that physics-augmented neural networks offer also for multi-physical material modeling such as nonlinear electro-elasticity.
Close