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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)
Filippo Masi, Ioannis Stefanou
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 398, pp. 115190-115190
Open Access | Times Cited: 95

Showing 1-25 of 95 citing articles:

A new family of Constitutive Artificial Neural Networks towards automated model discovery
Kevin Linka, Ellen Kuhl
Computer Methods in Applied Mechanics and Engineering (2022) Vol. 403, pp. 115731-115731
Open Access | Times Cited: 143
Neural networks meet hyperelasticity: A guide to enforcing physics
Lennart Linden, Dominik K. Klein, Karl A. Kalina, et al.
Journal of the Mechanics and Physics of Solids (2023) Vol. 179, pp. 105363-105363
Open Access | Times Cited: 72
FE$${}^\textrm{ANN}$$: an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining
Karl A. Kalina, Lennart Linden, Jörg Brummund, et al.
Computational Mechanics (2023) Vol. 71, Iss. 5, pp. 827-851
Open Access | Times Cited: 57
Deep active learning for constitutive modelling of granular materials: From representative volume elements to implicit finite element modelling
Tongming Qu, Shaoheng Guan, Y.T. Feng, et al.
International Journal of Plasticity (2023) Vol. 164, pp. 103576-103576
Open Access | Times Cited: 46
Deep learning in computational mechanics: a review
Leon Herrmann, Stefan Kollmannsberger
Computational Mechanics (2024) Vol. 74, Iss. 2, pp. 281-331
Open Access | Times Cited: 28
A Review on Data-Driven Constitutive Laws for Solids
Jan N. Fuhg, Govinda Anantha Padmanabha, Nikolaos Bouklas, et al.
Archives of Computational Methods in Engineering (2024)
Closed Access | Times Cited: 25
Theory and implementation of inelastic Constitutive Artificial Neural Networks
Hagen Holthusen, Lukas Lamm, Tim Brepols, et al.
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 428, pp. 117063-117063
Open Access | Times Cited: 22
Multiscale Thermodynamics-Informed Neural Networks (MuTINN) towards fast and frugal inelastic computation of woven composite structures
M. El Fallaki Idrissi, Francis Praud, Fodil Meraghni, et al.
Journal of the Mechanics and Physics of Solids (2024) Vol. 186, pp. 105604-105604
Open Access | Times Cited: 16
On the use of physics-based constraints and validation KPI for data-driven elastoplastic constitutive modelling
Rúben Lourenço, Aiman Tariq, Pétia Georgieva, et al.
Computer Methods in Applied Mechanics and Engineering (2025) Vol. 437, pp. 117743-117743
Open Access | Times Cited: 3
A comparative study on different neural network architectures to model inelasticity
Max Rosenkranz, Karl A. Kalina, Jörg Brummund, et al.
International Journal for Numerical Methods in Engineering (2023) Vol. 124, Iss. 21, pp. 4802-4840
Open Access | Times Cited: 35
Automated model discovery for skin: Discovering the best model, data, and experiment
Kevin Linka, Adrián Buganza Tepole, Gerhard A. Holzapfel, et al.
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 410, pp. 116007-116007
Open Access | Times Cited: 32
Physically recurrent neural networks for path-dependent heterogeneous materials: Embedding constitutive models in a data-driven surrogate
M.A. Maia, I.B.C.M. Rocha, Pierre Kerfriden, et al.
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 407, pp. 115934-115934
Open Access | Times Cited: 31
Evolution TANN and the identification of internal variables and evolution equations in solid mechanics
Filippo Masi, Ioannis Stefanou
Journal of the Mechanics and Physics of Solids (2023) Vol. 174, pp. 105245-105245
Open Access | Times Cited: 30
Two-stage 2D-to-3D reconstruction of realistic microstructures: Implementation and numerical validation by effective properties
Paul Seibert, Alexander Raßloff, Karl A. Kalina, et al.
Computer Methods in Applied Mechanics and Engineering (2023) Vol. 412, pp. 116098-116098
Open Access | Times Cited: 27
Data-driven multiscale modelling of granular materials via knowledge transfer and sharing
Tongming Qu, Jidong Zhao, Shaoheng Guan, et al.
International Journal of Plasticity (2023) Vol. 171, pp. 103786-103786
Closed Access | Times Cited: 23
Thermodynamics of Learning Physical Phenomena
Elías Cueto, Francisco Chinesta
Archives of Computational Methods in Engineering (2023) Vol. 30, Iss. 8, pp. 4653-4666
Open Access | Times Cited: 22
NN‐mCRE: A modified constitutive relation error framework for unsupervised learning of nonlinear state laws with physics‐augmented neural networks
Antoine Benady, Emmanuel Baranger, Ludovic Chamoin
International Journal for Numerical Methods in Engineering (2024) Vol. 125, Iss. 8
Open Access | Times Cited: 14
Micromechanics-based deep-learning for composites: Challenges and future perspectives
Mohsen Mirkhalaf, I.B.C.M. Rocha
European Journal of Mechanics - A/Solids (2024) Vol. 105, pp. 105242-105242
Open Access | Times Cited: 14
Unsupervised learning of history-dependent constitutive material laws with thermodynamically-consistent neural networks in the modified Constitutive Relation Error framework
Antoine Benady, Emmanuel Baranger, Ludovic Chamoin
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 425, pp. 116967-116967
Open Access | Times Cited: 11
Viscoelasticty with physics-augmented neural networks: model formulation and training methods without prescribed internal variables
Max Rosenkranz, Karl A. Kalina, Jörg Brummund, et al.
Computational Mechanics (2024) Vol. 74, Iss. 6, pp. 1279-1301
Open Access | Times Cited: 11
Neural Network-augmented Differentiable Finite Element Method for Boundary Value Problems
Xi Wang, Zhen‐Yu Yin, Wei Wu, et al.
International Journal of Mechanical Sciences (2024) Vol. 285, pp. 109783-109783
Closed Access | Times Cited: 11
Artificial intelligence in metal forming
Jian Cao, Markus� Bambach, Marion Merklein, et al.
CIRP Annals (2024) Vol. 73, Iss. 2, pp. 561-587
Closed Access | Times Cited: 10
Spiking neural networks for nonlinear regression
Alexander Henkes, Jason K. Eshraghian, Henning Wessels
Royal Society Open Science (2024) Vol. 11, Iss. 5
Open Access | Times Cited: 9
A thermodynamics-informed neural network for elastoplastic constitutive modeling of granular materials
Mingming Su, Yu You, T.H. Chen, et al.
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 430, pp. 117246-117246
Closed Access | Times Cited: 9
On the application of Physically-Guided Neural Networks with Internal Variables to Continuum Problems
Jacobo Ayensa-Jiménez, Mohamed H. Doweidar, José A. Sanz-Herrera, et al.
Mechanics of Materials (2025) Vol. 205, pp. 105317-105317
Closed Access | Times Cited: 1
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