Elías Cueto. Publications

Books

  • E. Cueto, D. González , B. Moya. Teoría de Estructuras para arquitectos. Prensas Universitarias de Zaragoza, 2023. [link]
  • E. Cueto, D. González. An introduction to structural mechanics for architects. Springer, 2018. [link]
  • D. González , E. Cueto. Resistencia de Materiales para arquitectos. Prensas Universitarias de Zaragoza, 2018. [link]
  • E. Cueto, D. Gonzalez, I. Alfaro. Proper Generalized Decompositions. An Introduction to Computer Implementation with Matlab. Springer, 2016.[link]
  • F. Chinesta, E. Cueto. PGD-Based Modeling of Materials, Structures and Processes. Springer, 2014.[link]
  • F. Chinesta, S. Cescotto, E. Cueto, P. Lorong. La methode des elements naturels en calcul des structures et simulation des procedes. Lavoisier, Paris, 2010.  [link]
  • F. Chinesta, S. Cescotto, E. Cueto, P. Lorong. Method of Natural Element for the simulation of structures and processes. ISTE-Wiley, London, 2011. [link]

Edited books

  • E. Cueto, F. Chinesta, editors. Proceedings of the 10th ESAFORM conference on material forming. American Institute of Physics, College Park, Maryland. Vol. 907. ISBN 978-0-7354-0414-4. 2007. [link]
  • F. Chinesta, E. Cueto, editors. Advances in material forming. Springer. Paris, France. ISBN 98-2-287-72142-7. 2007. [link]
  • F. Chinesta, E. Cueto, E. Abisset-Chavanne. Proceedings of the 19th ESAFORM conference. Nantes, France, 2016. AIP Conference proceedings, 2016. [link]
  • Francisco Chinesta, Elías Cueto, Yohan Payan, Jacques Ohayon (Eds.) Reduced Order Models for the Biomechanics of Living Organs. Elsevier, 2023. [Download from publisher].

Refereed Journal Publications

Forthcoming

  1. A comparison of Single and Double Generator Formalisms for Thermodynamics-Informed Neural Networks. P. Urdeitx, I. Alfaro, D. Gonzalez, F. Chinesta, E. Cueto. Submitted, 2024. [Arxiv preprint]
  2. A Neural Network Architecture for Physically-consistent Haptic Rendering. Q. Hernandez, P. Martins, L. Tesan, I. Alfaro, D. Gonzalez, F. Chinesta, E. Cueto. Submitted, 2024.
  3. Thermodynamics-informed super-resolution of scarce temporal dynamics data. Carlos Bermejo-Barbanoj, Beatriz Moya, Alberto Badías, Francisco Chinesta, Elías Cueto. Submitted, 2024. [Arxiv preprint].
  4. Learning models from efficient data-assimilation: application to inverse problems. Daniele Di Lorenzo, Victor Champaney, Chady Ghnatios, Elias Cueto, and Francisco Chinesta. Submitted, 2023. [Download PDF of draft]
  5. Optimal trajectory planning combining model-based and data-driven hybrid approaches. Chady Ghnatios, Daniele Di Lorenzo, Victor Champaney, Amine Ammar, Elias Cueto and Francisco Chinesta. Advanced Modeling and Simulation in Engineering Sciences, accepted for publication, 2024. [Download PDF of draft]
  6. Structure-preserving formulations for data-driven analysis of coupled multi-physics systems. Alba Muixí, David González, Francisco Chinesta, Elías Cueto. Submitted, 2023. 
  7. Computational sensing, understanding, and reasoning: an artificial intelligence approach to physics-informed world modeling. Beatriz Moya, Alberto Badias, David Gonzalez, Francisco Chinesta, Elias Cueto. Archives of Computational Methods in Engineering, accepted for publication, 2023. [Download PDF of draft]
  8. Conciliating accuracy and efficiency to empower engineering based on performance: a short journey. Francisco Chinesta, Elias Cueto. Comptes Rendus Mécanique. Special issue on the occasion of the French year of Mechanics, accepted for publication, 2023. [Download from publisher, OA], 2023.

2024

  1. An indirect training approach for implicit constitutive modelling using recurrent neural networks and the virtual fields method. Ruben Lourenço, Petia Georgieva, Elias Cueto, A. Andrade-Campos. Computer Methods in Applied Mechanics and Engineering, 425, 116961, 2024. [Download PDF of draft] [Download from publisher, OA]
  2. Thermodynamics-informed Graph Neural Networks. Q. Hernández, A. Badías, F. Chinesta, E. Cueto. IEEE Transactions on Artificial Intelligence, vol. 5, no. 3, pp. 967-976, March 2024, doi: 10.1109/TAI.2022.3179681. [arXiv preprint][Download from publisher]
  3. Casting Hybrid Twin: Physics-based reduced order models enriched with data-driven models enabling the highest accuracy in real-time. Amine Ammar, Mariem Bensaada, Elias Cueto, Francisco Chinesta. International Journal of Material Forming, 17, 16 (2024). [Link to publisher]

2023

  1. Thermodynamics of learning physical phenomena. Elías Cueto and Francisco Chinesta. Archives of Computational Methods in Engineering, 30, 4653–4666 (2023). [Arxiv preprint, download from publisher].
  2. Machine Learning in Computer Aided Engineering. Francisco J. Montáns, Elías Cueto, and Klaus-Jürgen Bathe. Machine Learning in Modeling and Simulation, in T. Rabczuk, K.-J. Bathe, Eds. Springer, Cham. [Open Access], 2023.
  3. Optimal velocity planning based on the solution of the Euler-Lagrange equations with a neural-network-based velocity regression. Ch. Ghnatios, D. de Lorenzo, V. Champaney, E. Cueto, F. Chinesta. Discrete and Continuous Dynamical Systems, early view, 2023. doi: 10.3934/dcdss.2023080 [download from publisher]
  4. Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems. Quercus Hernandez, Alberto Badias, Francisco Chinesta and Elias Cueto. Computational Mechanics 72, 553–561 (2023). https://doi.org/10.1007/s00466-023-02296-w. [Arxiv preprint] [Download from publisher, OA]
  5. A thermodynamics-informed active learning approach to perception and reasoning about fluids. B. Moya, A. Badias, D. Gonzalez, F. Chinesta, E. Cueto. Comput Mech (2023). https://doi.org/10.1007/s00466-023-02279-x. [arXiv preprint] [Project video] [Download from publisher, Open Access]
  6. Regularized regressions for parametric models based on separated representations. Abel Sancarlos, Victor Champaney, Elias Cueto, Francisco Chinesta. Advanced Modeling and Simulation in Engineering Sciences, 10:4, 2023. [Download from publisher, Open Access]
  7. Thermodynamics-informed neural networks for physically realistic mixed reality. Quercus Hernández, Alberto Badías, Francisco Chinesta, Elías Cueto. Computer Methods in Applied Mechanics and Engineering, Volume 407, 2023, 115912. [Arxiv preprint] [Download from the publisher, Open Access]
  8. Physics perception in sloshing scenes with guaranteed thermodynamic consistency. B. Moya, A. Badias, D. Gonzalez, F. Chinesta, E. Cueto. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2), 2136-2150, 2023. [Project video] [arXiv preprint] [download from publisher].

2022

  1. Empowering Engineering with Data, Machine Learning and Artificial Intelligence: A Short Introductive Review. F. Chinesta, E. Cueto. In press, Advanced Modeling and Simulation in Engineering Sciences, 9:21, 2022. [Download PDF of draft. Open Access]
  2. Modeling systems from partial observations. Victor Champaney, Víctor Jesús Amores Medianero, Sevan Garois, Luis Irastorza-Valera, Chady Ghnatios, Francisco Montans, Elías Cueto and Francisco Chinesta. Frontiers Materials, 9:970970, 2022. [Open Access]
  3. MORPH-DSLAM: Model Order Reduction for PHysics-based Deformable SLAM. A. Badias, I. Alfaro, D. Gonzalez, F. Chinesta, E. Cueto. Accepted for publication, IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7764 – 7777. [arXiv preprint] [video]
  4. Data Completion, Model Correction and Enrichment Based on Sparse Identification and Data Assimilation. Daniele Di Lorenzo, Victor Champaney, Claudia Germoso, Elias Cueto and Francisco Chinesta. Appl. Sci. 2022, 12(15), 7458; https://doi.org/10.3390/app12157458. 
  5. Crossing Scales: Data-Driven Determination of the Micro-scale Behavior of Polymers From Non-homogeneous Tests at the Continuum-Scale. Víctor J. Amores, Francisco J. Montáns, Elías Cueto and Francisco Chinesta. Frontiers in Materials, 9:879614, 2022. [Open Access]
  6. Engineering empowered by physics-based and data-driven hybrid models: A methodological overview. V. Champaney, F. Chinesta, E. Cueto. International Journal of Material Forming 15 (3), 1-14, 2022. 
  7. Digital twins that learn and correct themselves. Beatriz Moya, Alberto Badías, Icíar Alfaro, Francisco Chinesta, Elías Cueto. International Journal for Numerical Methods in Engineering, 123(13), 3034-3044, 2022. [Download PDF of draft]

2021

  1. Learning stable reduced-order models for hybrid twins. A. Sancarlos, M. Cameron, J.-M. Le Peuvedic, J. Groulier, J-L. Duval, E. Cueto and F. Chinesta. Data-Centric Engineering, 2: e10, 2021. [Download from arXiv][Link to publisher (OA)] 
  2. Monitoring weeder robots and anticipating their functioning by using advanced topological data analysis. T. Frahi, A. Sancarlos, M. Galle, X. Beaulieu, A. Chambard, A. Falco, E. Cueto, F. Chinesta. Frontiers in Artificial Intelligence, 4:761123, 2021. [arXiv preprint]
  3. A Separated Representation involving Multiple Time Scales within the Proper Generalized Decomposition framework. Angelo Pasquale; Amine Ammar; Antonio Falco; Simona Perotto; Elias Cueto; Jean Louis Duval; Francisco Chinesta. Advanced Modeling and Simulation in Engineering Sciences (2021)8:26[Available as MOX report] [Link to the publisher (OA)]
  4. Deep learning of thermodynamics-aware reduced-order models from data. Quercus Hernandez, Alberto Badias, David Gonzalez, Francisco Chinesta, Elias Cueto. Computer Methods in Applied Mechanics and Engineering, Volume 379, 113763, 2021. [Download draft from arXiv:2007.03758] [Download from publisher] [GitHub project] [Project Video].
  5. Learning data-driven reduced elastic and inelastic models of spot-welded patches. Agathe Reille, Victor Champaney, Fatima Daim, Yves Tourbier, Nicolas Hascoet, David Gonzalez, Elias Cueto, Jean Louis Duval, Francisco Chinesta. Mechanics and industry, 22, 32, 2021. [Download from publisher, Open Access]
  6. Spurious-free interpolations for non-intrusive PGD-based parametric solutions: Application to composites forming processes. Chady Ghnatios, Elias Cueto,  Antonio Falco, Jean-Louis Duval, Francisco Chinesta. International Journal of Material Forming, 14, pages 83–95 (2021). [Link to publisher]
  7. From ROM of electrochemistry to AI-based battery digital and hybrid twin. A.Sancarlos, M. Cameron, A. Abel,  E. Cueto,  J.-L. Duval, F. Chinesta. Archives of Computational Methods in Engineering, 28, 979–1015, 2021. [Download PDF of draft]
  8. Empowering Advanced Driver-Assistance Systems from Topological Data Analysis. Tarek Frahi, Francisco Chinesta, Antonio Falco, Alberto Badias, Elias Cueto, Hyung Yun Choi, Manyong Han, Jean-Louis Duval. Mathematics, 9(6), 634, 2021. [Download from publisher, OA]
  9. Fast computation of multi-parametric electromagnetic fields in synchronous machines by using PGD-based fully separated representations. Abel Sancarlos, Chady Ghnatios, Jean-Louis Duval, Nicolas Zerbib, Elias Cueto and Francisco Chinesta. Energies, 2021, 14, 1454, 2021. [Download from publisher (OA)]
  10. A novel sparse reduced order formulation for modeling electromagnetic forces in electric motors. A. Sancarlos, E. Cueto, F. Chinesta, J.-L. Duval. Springer-Nature Applied Sciences 3, Article number: 355 (2021). [Download pdf (Open Access)].
  11. Structure-preserving neural networks. Q. Hernandez, A. Badias, D. Gonzalez, F. Chinesta, E. Cueto. Journal of Computational Physics, Volume 426, 109950, 2021. [Arxiv preprint, link to the publisher, GitHub project page]
  12. Learning non-Markovian physics from data. D. Gonzalez, F. Chinesta, E. Cueto. Journal of Computational Physics, Volume 428, 109982. 2021. [Download PDF of draft]

2020

  1. Numerical experiments on manifold learning, manifolds dimension and latent variables. Ruben Ibanez, Pierre Gilormini, Elias Cueto and Francisco Chinesta. Comptes Rendus Mécanique. 348 (2020) no. 10-11, pp. 937-958. [Download PDF of draft] [Link to publisher]
  2. On the effective conductivity and the apparent viscosity of a thin rough polymer interface. Amine Ammar, Chady Ghnatios, Frank Delplace, Anais Barasinski,  Jean-Louis Duval, Elías Cueto, Francisco Chinesta. International Journal for Numerical Methods in Engineering, 121 (23), 5256-5274, 2020. [Download PDF of draft]
  3. A novel approach for sensitivity analysis of Friction Spot Joining Process on Aluminum\Polycarbonate sheet from simulation runs. Giuseppe Serratore, Francesco Gagliardi, Clara Argerich Martin, Ruben Ibanez Pinilo, Elias Cueto, Luigino Filice and Francisco Chinesta. International Journal of Material Forming, 13, 5, 737-747, 2020.
  4. Real-time interaction of virtual and physical objects in Mixed Reality applications. A. Badias, I. Alfaro, D. Gonzalez, F. Chinesta, E. Cueto. International Journal for Numerical Methods in Engineering, 121, 17, 3849-3868, 2020. [Download PDF of draft]
  5. Physically sound, self-learning digital twins for sloshing fluids. B. Moya, I. Alfaro, D. Gonzalez, F. Chinesta, E. Cueto. PLoS ONE, 15(6): e0234569, 2020. [Download paper]
  6. Nonlinear regression operating on microstructures described from Topological Data Analysis for the real-time prediction of effective properties. Minyoung Yun, Clara Argerich, Elías Cueto, Jean-Louis Duval, Francisco Chinesta. Materials 2020, 13(10), 2335. [Download from publisher, Open Access]
  7. A data-driven learning method for constitutive modeling: application to vascular hyperelastic soft tissues. D. Gonzalez, A. Garcia-Gonzalez, F. Chinesta, E. Cueto. Materials, 13(10), 2319, 2020. [Download PDF of draft][Download from publisher, Open Access]
  8. Virtual, Digital and Hybrid Twins. A new paradigm in data-based engineering and engineered data. F. Chinesta,  E. Cueto, E. Abisset-Chavanne, J. L. Duval,  F. El Khaldi. Archives of Computational Methods in Engineering, 27:105-134, 2020 [Download PDF of draft]

2019

  1. Incremental Dynamic Mode Decomposition: A reduced-model learner operating at the low-data limit. A. Reille, N. Hascoet, Ch. Ghnatios, A. Ammar, E. Cueto, J.-L. Duval, F. Chinesta, R. Keunings. Comptes Rendus Mécanique, 347 (11), 780-792, 2019. [Download PDF of draft]
  2. Data-driven GENERIC modeling of poroviscoelastic materials. Chady Ghnatios, Iciar Alfaro, David González, Francisco Chinesta and Elias Cueto. Entropy 21 (12), 1165, 2019. [Download PDF from publisher (Open Access)]
  3. Some applications of compressed sensing in computational mechanics. Model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction. R. Ibanez, E. Abisset-Chavanne, E. Cueto, A. Ammar, J.-L. Duval, F. Chinesta. Computational Mechanics, 64:1259–1271, 2019. [Download PDF of draft]
  4. Multi Scale Proper Generalized Decomposition based on the Partition of Unity. Rubén Ibáñez, Amine Ammar, Elías Cueto, Antonio Huerta, Jean-Louis Duval, Francisco Chinesta. International journal for numerical methods in engineering, 2019;120:727–747. [Download PDF of draft]
  5. A local, mutiple Proper Generalized Decomposition based on the Partition of Unity. R. Ibañez, E. Abisset-Chavanne, F. Chinesta, A. Huerta, E. Cueto. International Journal for Numerical Methods in Engineering, 120(2), 139-152, 2019. [Download PDF of draft]
  6. An Augmented Reality platform for interactive aerodynamic design and analysis. A. Badias, S. Curtit, D. Gonzalez, I. Alfaro, F. Chinesta, E. Cueto. International Journal for Numerical Methods in Engineering, 120:125–138, 2019. [Download PDF of draft, documentary video]
  7. Hybrid Constitutive Modeling: Data-driven learning of corrections to plasticity models. R. Ibañez, E. Abisset-Chavanne, D. Gonzalez, J. L. Duval, E. Cueto, F. Chinesta, International Journal of Material Forming, 12(4), 717–725, 2019. [Download PDF of draft]
  8. Advanced spatial separated representations. Chady Ghnatios, Emmanuelle Abisset-Chavanne, Amine Ammar, EliasCueto, Jean-Louis Duval, Francisco Chinesta. Computer Methods in Applied Mechanics and Engineering, 354, 802-819 , 2019. [Download PDF of draft]
  9. Learning slosh dynamics by means of data. B. Moya, D. Gonzalez, I. Alfaro, F. Chinesta, E. Cueto. Computational Mechanics, 64, Issue 2, pp 511–52, 2019. [Download PDF of draft]
  10. From linear to nonlinear parametric structural dynamics. Giacomo Quaranta, Clara Argerich Martin, Ruben Ibañez, Emmanuelle Abisset-Chavanne, Jean Louis Duval, Elias Cueto, Francisco Chinesta. Comptes Rendus Academie de Sciences-Mecanique, 347(5), 445-454, 2019. [Download PFD of draft]
  11. Thermodynamically consistent data-driven computational mechanics. D. González, F. Chinesta, E. Cueto. Continuum Mechanics and Thermodynamics, 31 (1), pp 239–253, 2019. [Download PDF of draft]
  12. Learning corrections for hyperelastic models from data. David Gonzalez, Francisco Chinesta and Elias Cueto. Frontiers Materials. volume 6, article 14, 2019. [Download PDF of draft, Download from publisher (Open Access)]

2018

  1. Data-Driven Upscaling Of Orientation Kinematics In Suspensions Of Rigid Fibres. Adrien Scheuer, Amine Ammar, Emmanuelle Abisset-Chavanne, Elias Cueto, Francisco Chinesta, Roland Keunings, Suresh G. Advani. Computer Modeling in Engineering Sciences, Vol.117, No.3 ,pp.367-386 . [Download PDF of draft]
  2. A multi-dimensional data-driven sparse identification technique: the sparse Proper Generalized Decomposition. R. Ibañez, E. Abisset-Chavanne, A. Ammar, D. González, E. Cueto, A. Huerta, J. L. Duval and F. Chinesta. Complexity, 2018. Paper 5608286. [Download PDF of draft] [Download from publisher (Open Access) here]
  3. Reduced order modeling for physically-based augmented reality. A. Badías, I. Alfaro, D. Gonzalez, F. Chinesta, E. Cueto. Computer Methods in Applied Mechanics and Engineering, 341, p 53-70, 2018. [Download pdf of draft] [video1][video2][video3]
  4. Wavelet-Based Multiscale Proper Generalized Decomposition. A. León, A. Barasinski, E. Abisset-Chavanne, E. Cueto, F. Chinesta. Comptes Redus de l’Academie des Sciences de Paris-Mecanique, 346 (7), 485-500, 2018. [Download pdf of draft]
  5. On the physical interpretation of fractional diffusion. F. Chinesta, E. Abisset-Chavanne, E. Nadal, E. Cueto. Comptes Rendus de l’Academie des Sciences de Paris-Mecanique, 346 (7), 581-589, 2018. [Download PDF of draft]
  6. Haptic simulation of tissue tearing during surgery. C. Quesada, I. Alfaro, D. Gonzalez, F. Chinesta, E. Cueto. International Journal for Numerical Methods in Biomedical Engineering, 34 (3), e2926. [Download pdf of draft][video1][video2]
  7. Reduced-order modeling of soft robots. Jean Chenevier, David Gonzalez, Jose Vicente Aguado, Francisco Chinesta and Elias Cueto. PLoS ONE, 13(2): e0192052, 2018. [Download PDF of draft] [OpenAccess]
  8. A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity. R. Ibañez, E. Abisset-Chavanne, J. V. Aguado, D. Gonzalez, E. Cueto, F. Chinesta. Archives of Computational Methods in Engineering, 25(1), 47-57, 2018. [Download pdf of draft].
  9. A manifold learning approach for Integrated Computational Materials Engineering. E. Lopez, D. Gonzalez, J.V. Aguado, E. Abisset-Chavanne, F. Lebel, R. Upadhyay, E. Cueto, C. Binetruy, F. Chinesta. Archives of Computational Methods in Engineering, 25(1), 59-68, 2018. [Download pdf of draft]
  10. kPCA-based Parametric Solutions within the PGD Framework. D. Gonzalez, J.V. Aguado, E. Cueto, E. Abisset-Chavanne, F. Chinesta. Archives of Computational Methods in Engineering, 25(1), 69-86, 2018. [Download pdf of draft]

2017

  1. Local Proper Generalized Decomposition. A. Badías, D. González, I. Alfaro, F. Chinesta, E. Cueto. International Journal for Numerical Methods in Engineering, 112:12,1715–1732, 2017. [Download pdf of draft]
  2. Model order reduction for real-time data assimilation through Extended Kalman Filters. D. Gonzalez, A. Badias, I. Alfaro, F. Chinesta, E. Cueto. Computer Methods in Applied Mechanics and Engineering, 326, 679-693, 2017. [Download pdf of draft]
  3. A Physically-Based Fractional Diffusion Model for Semi-Dilute Suspensions of Rods in a Newtonian Fluid. E. Nadal, J. V. Aguado, E. Abisset, R. Keunings, E. Cueto, F. Chinesta. Applied Mathematical Modelling, 51, 58-67, 2017. [Download pdf of draft].
  4. Data-driven non-linear elasticity. Constitutive manifold construction and problem discretization. R. Ibañez, D. Borzacchiello, J. V. Aguado, E. Abisset-Chavanne, E. Cueto, P. Ladeveze, F. Chinesta. Computational Mechanics, 60 (5), 813–826, 2017. [Download pdf of draft]

2016

  1. Computational vademecums for real-time simulation of surgical cutting in haptic environments. C. Quesada, D. Gonzalez, I. Alfaro, E. Cueto and F. Chinesta. International Journal for Numerical Methods in Engineering. 108 (10), 1230-1247, 2016. [Download pdf of draft]
  2. Vademecum-based GFEM (V-GFEM): Optimal Enrichment for transient problems. D. Canales, A. Leygue, F. Chinesta, D. Gonzalez, E. Cueto, E. Feulvarch, J.-M. Bergheau, A. Huerta. International Journal for Numerical Methods in Engineering, 108(9), 971-989, 2016. [Download pdf of draft]
  3. Real-time simulation techniques for augmented learning in science and engineering. C. Quesada, D. Gonzalez, I. Alfaro, E. Cueto, A. Huerta and F. Chinesta. The Visual Computer, 32(11), 1465-1479, 2016. [Download pdf of draft] [video]
  4. In-plane-out-of-plane separated representations of updated-Lagrangian descriptions of thermomechanical models defined in plate domains. D. Canales, A. Leygue, F. Chinesta, I. Alfaro, D. Gonzalez, E. Cueto, E. Feulvarch and J.M. Bergheau. Comptes Rendus Mecanique, 344, 4-5, 225-235, 2016. [Download pdf of draft]
  5. Chemical Master Equation Empirical Moment Closure. A. Ammar, M. Magnin, O. Roux, E. Cueto, F. Chinesta. Biological Sciences open access, 5:155. doi:10.4172/2329-6577.1000155, 2016. [Download pdf of draft]
  6. On the use of model order reduction for simulating automated fibre placement processes. Nicolas Bur, Pierre Joyot, Chady Ghnatios, Pierre Villon, Elias Cueto, Francisco Chinesta. Advanced Modeling and Simulation in Engineering Sciences (AMSES), 3:4, 2016. [Download pdf of paper (OA)]
  7. Computational patient avatars for surgery planning. D. Gonzalez, E. Cueto and F. Chinesta. Annals of Biomedical Engineering, 44(1), 35-45. 2016. [Download pdf of draft]

2015

  1. Effect of the separated approximation of input data in the accuracy of the resulting PGD solution. Sergio Zlotnik, Pedro Diez, Elias Cueto, David Gonzalez and Antonio Huerta. Advanced Modeling and Simulation in Engineering Sciences (AMSES), 2:28, 2015. [Download pdf of draft]
  2. Towards a pancreatic surgery simulator based on model order reduction. Andres Mena, David Bel, Iciar Alfaro, David Gonzalez, Elias Cueto, and Francisco Chinesta. Advanced Modeling and Simulation in Engineering Sciences (AMSES), 2:31, 2015. [Download pdf of draft][video]
  3. An error estimator for real-time simulators based on model order reduction. Iciar Alfaro, David Gonzalez, Sergio Zlotnik, Pedro Diez, Elias Cueto, and Francisco Chinesta. Advanced Modeling and Simulation in Engineering Sciences (AMSES), 2:30, 2015. [Download pdf of draft]
  4. Fast and reliable gate arrangement pre-design of resin infusion processes. F. Sanchez, L. Domenech, V. Garcia, N. Montes, A. Falco, E. Cueto, F. Chinesta, P. Fideu. Composites A, 77, p. 285-292, 2015. [Download pdf of draft]
  5. A Second-Gradient Theory of Dilute Suspensions of Flexible Rods in a Newtonian Fluid. E. Abisset-Chavanne, J. Ferec, G. Aussias, E. Cueto, F. Chinesta, R. Keunings. Archives of Computational Methods in Engineering, 22, 511-527, 2015. [Download pdf of draft]
  6. Efficient stabilization of advection terms involved in separated representations of Boltzmann and Fokker-Planck equations. F. Chinesta, E. Abisset-Chavanne, A. Ammar, E. Cueto. Communications in Computational Physics, 17 (4), pp. 975-1006, 2015. [Download pdf of draft]
  7. Real-time monitoring of thermal processes by reduced order modeling. Jose V. Aguado, Antonio Huerta, Francisco Chinesta and Elias Cueto. International Journal for Numerical Methods in Engineering, 102(5), 991-1017, 2015. [Download pdf of draft]
  8. Kinetic Theory Modeling and Efficient Numerical Simulation of Gene Regulatory Networks Based on Qualitative Descriptions. Francisco Chinesta, Morgan Magnin, Olivier Roux, Amine Ammar, Elias Cueto, Entropy 2015, 17(4), 1896-1915; doi:10.3390/e17041896. [Download Open Access PDF]
  9. Meshless methods for the simulation of material forming. A review. E. Cueto, F. Chinesta. International Journal of Material Forming, 8(1), 25-43, 2015. [Download pdf of draft]
  10. Fractional modeling of functionalized CNT suspensions. J.V. Aguado, E. Abisset-Chavanne, E. Cueto, F. Chinesta, R. Keunings. Rheologica Acta, 54, 109-119, 2015. [Download pdf of draft]
  11. Computational vademecums for the real-time simulation of haptic collision between nonlinear solids. D. Gonzalez, I. Alfaro, C. Quesada, E. Cueto, F. Chinesta. Computer Methods in Applied Mechanics and Engineering, 283 (2015) 210–223. [Download pdf of draft] [video]

2014

  1. Un método de descomposición propia generalizada para operadores diferenciales de alto orden. Carlos Quesada, Guangtao Xu, David González, Icíar Alfaro, Adrien Leygue, Michel Visonneau, Elías Cueto, Francisco Chinesta. Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, 2014. DOI: 10.1016/j.rimni.2014.09.001 [Download pdf of draft].
  2. Real-time direct integration of reduced solid dynamics equations. David Gonzalez, Elias Cueto, Francisco Chinesta. International Journal for Numerical Methods in Engineering, 99 (9), pp. 633–653, 2014. [Download pdf of draft].
  3. Real time simulation for Computational Surgery: A review. E. Cueto, F. Chinesta. Advanced Modeling and Simulation in Engineering Sciences, 1-11, 2014. [Published in Open Acces: link]
  4. Real-time in silico experiments on gene regulatory networks and surgery simulation on handheld devices. I. Alfaro, D. Gonzalez, F. Bordeu, A. Leygue, A. Ammar, E. Cueto, F. Chinesta. Journal of Computational Surgery, 1, 1-1 (Open acces). [Download pdf from the publisher]
  5. Parametric solutions involving geometry: a step towards efficient shape optimization. A. Ammar, A. Huerta, A. Leygue, F. Chinesta, E. Cueto. Computer Methods in Applied Mechanics and Engineering, 68(1), 178-193, 2014.[Download pdf of draft]

2013

  1. Model order reduction in hyperelasticity: a Proper Generalized Decomposition approach. S. Niroomandi, D. Gonzalez, I. Alfaro, E. Cueto, F. Chinesta. International Journal for Numerical Methods in Engineering, 96, 3, 129-149. 2013. [Download pdf of draft] [video] [video]
  2. NEM-FEM Comparison on Porthole Die Extrusion of AA-6082. G. Ambrogio, F. Gagliardi, L. Filice, I. Alfaro, E. Cueto. Journal of Mechanical Science and Technology, 27(4), 1089-1095, 2013.
  3. Streamline upwind/Petrov-Galerkin-based stabilization of Proper Generalized Decompositions for high-dimensional Advection-Diffusion Equations. D. Gonzalez, E. Cueto, P. Diez, A. Huerta, F. Chinesta. International Journal for Numerical Methods in Engineering, 94(13), 1216-1232, 2013. [Download pdf of draft]
  4. Real-time simulation of biological soft tissues: a PGD approach. S. Niroomandi, D. Gonzalez, I. Alfaro, F. Bordeu, A. Leygue, E. Cueto, F. Chinesta. International Journal for Numerical Methods in Biomedical Engineering, 29(5), 586-600, 2013. [Download pdf of draft]
  5. PGD-based computational vademecum for efficient design, optimization and control. F. Chinesta, A. Leygue, F. Bordeu, J.V. Aguado, E. Cueto, D. Gonzalez, I. Alfaro, A. Ammar, A. Huerta. Archives of Computational Methods in Engineering, 20(1), 31-59, 2013. [Download pdf of draft]
  6. Non incremental PGD solution of parametric uncoupled models defined in evolving domains. A. Ammar, E. Cueto, F. Chinesta. International Journal for Numerical Methods in Engineering, 8(24), 887-904, 2013.[Download pdf of draft].
  7. Una estrategia basada en la descomposicion propia generalizada para aplicaciones gobernadas por datos dinamicos (In spanish) F. Masson, D. Gonzalez, E. Cueto, F. Chinesta. Revista Internacional de Metodos Numericos para Calculo y Diseño en Ingenieria, 29(2), 2013. [Download pdf of draft]

2012

  1. A natural neighbour Lagrange-Galerkin method for the simulation of Newtonian and Oldroyd-B free-surface flows. A. Galavis, D. Gonzalez, E. Cueto. International Journal for Numerical Methods in Fluids, 70 (7), 860–885. 2012 [Download pdf of draft]
  2. Reduction of the chemical master equation for gene regulatory networks using proper generalized decompositions. A. Ammar, E. Cueto, F. Chinesta, International Journal for Numerical Methods in Biomedical Engineering, 28(9), 960-973, 2012. [Download pdf of draft]
  3. Proper Generalized Decomposition Based Dynamic Data-Driven Control of Thermal Processes. Ch. Ghnatios, F. Masson, A. Huerta, A. Leygue, E. Cueto, F. Chinesta. Computer Methods in Applied Mechancis and Engineering, Volumes 213–216, 1 March 2012, Pages 29-41, 2012.[Download pdf of draft]
  4. Accounting for large deformations in real-time simulations of soft tissues based on reduced-order models. Siamak Niroomandi, Iciar Alfaro, Elias Cueto, Francisco Chinesta. Computer Methods and Programs in Biomedicine, Volume 105, Issue 1, Pages 1-12, 2012. [download pdf of draft]
  5. Proper Generalized Decomposition based Dynamic Data Driven Inverse Identification. D. González, F. Masson, F. Poulhaon, E. Cueto, F. Chinesta. Mathematics and Computers in Simulation, 82, p. 1677–1695, 2012. [Download pdf of draft]
  6. Proper Generalized Decomposition of time-multiscale models. Amine Ammar, Francisco Chinesta, Elías Cueto, Manuel Doblaré. International Journal for Numerical Methods in Engineering, 90(5), 569-596, 2012.[Download pdf of draft]
  7. Real-time simulation of surgery by reduced order modelling and X-FEM techniques. S. Niroomandi, I. Alfaro, D. Gonzalez, E. Cueto, F. Chinesta. International Journal for Numerical Methods in Biomedical Engineering, 28(5), 574-588, 2012. [Download pdf of draft]

2011

  1. A short review on model order reduction based on Proper Generalized Decomposition. F. Chinesta, P. Ladeveze, E. Cueto. Archives for Numerical Methods in Engineering, 18, 395-404, 2011. [Download pdf of draft]
  2. Methodological approach to efficient modeling and optimization of thermal processes taking place in a die: application to pultrusion. C. Ghnatios, F. Chinesta, E. Cueto, A. Leygue, A. Poitou, P. Breitkopf, P. Villon. Composites A. Volume 42, Issue 9, September 2011, Pages 1169-1178, 2011. http://dx.doi.org/10.1016/j.compositesa.2011.05.001
  3. A comparative study on the performance of meshless approximations and their integration. W. Quak, A.H. van den Boogaard, D. González, Elías Cueto. Computational Mechanics, 48, pp 121-137, 2011.
  4. Coupling finite elements and proper generalized decompositions. A. Ammar, F. Chinesta, E. Cueto. International Journal for Multiscale Computational Engineering, 9(1), 17-33, 2011.
  5. Meshless stochastic simulation of micro-macro kinetic theory models. E. Cueto, M. Laso, F. Chinesta. International Journal for Multiscale Computational Engineering, 9(1), 1-16, 2011.

2010

  1. Recent Advances and New Challenges in the Use of the Proper Generalized Decomposition for Solving Multidimensional Models. Francisco Chinesta, Amine Ammar, Elias Cueto. Archives for Numerical Methods in Engineering, 17(4), 327-350, 2010. [download pdf of draft]
  2. On the use of Proper Generalized Decompositions for multidimensional models. Francisco Chinesta, Amine Ammar, Elias Cueto. European Journal of Computational Mechanics, 19, pp 53-64, 2010. [download pdf of draft]
  3. Simulation of Porthole Die Extrusion Process comparing NEM and FEM modelling. I. Alfaro, F. Gagliardi, E. Cueto, L. Filice, F. Chinesta. Key Engineering Materials, 424, pp. 97-104, 2010.
  4. Proper Generalized Decomposition of Multiscale Models. Francisco Chinesta, Amine Ammar, Elias Cueto. International Journal for Numerical Methods in Engineering, 83(8-9), 1114-1132, 2010. [download pdf of draft]
  5. Model order reduction for hyperelastic materials. Siamak Niroomandi, Iciar Alfaro, Elias Cueto, Francisco Chinesta. International Journal for Numerical Methods in Engineering,81, 1180-1206, 2010.[download pdf of draft]
  6. Rheological modeling and forming process simulation of CNT nanocomposites. E. Cueto, R. Monge, F. Chinesta, A. Poitou, I. Alfaro, M. R. Mackley. International Journal of Material Forming, 3, pp 1327-1338, 2010.
  7. Recent advances on the use of separated representations. David Gonzalez, Amine Ammar, Francisco Chinesta and Elias Cueto. International Journal for Numerical Methods in Engineering, 81(5), 637-659, 2010. [download pdf of draft]
  8. A higher-order method based on local maximum entropy approximation. David Gonzalez, Elias Cueto, Manuel Doblare. International Journal for Numerical Methods in Engineering, 83, pp. 741-764, 2010.[download pdf of draft]

2009

  1. Coupling Finite Elements and Reduced Approximation Bases. A. Ammar, E. Pruliere, J. Ferec, F. Chinesta, E. Cueto. European Journal of Computational Mechanics, 18, 445-463, 2009.
  2. Non Incremental Strategies Based on Separated Representations: Applications in Computational Rheology. A. Ammar, M. Normandin F. Daim, D. Gonzalez, E. Cueto, F. Chinesta. Communications in Mathematical Sciences, 8(3), 671-695, 2010.
  3. Numerical Simulation of Friction Stir Welding by Natural Element Methods. I. Alfaro, G. Racineux, A. Poitou, E. Cueto, F. Chinesta. International Journal of Material Forming,2, 225-234, 2009. [download pdf of draft]
  4. Meshless methods with application to Liquid Composite Molding simulation. J. A. Garcia, Ll. Gascon, E. Cueto. I. Ordeig, F. Chinesta. Computer Methods in Applied Mechanics and Engineering,198, pp. 2700-2709, 2009.
  5. A preliminary comparison between finite element and meshless simulations of extrusion. L. Filice, I. Alfaro, F. Gagliardi, E. Cueto, F. Micari, F. Chinesta. Journal of Materials Processing Technology, 209(6), 3039-3049, 2009. [download pdf of draft]

2008

  1. Numerical simulation of spin coating processes involving functionalised Carbon nanotube suspensions. E. Cueto, A. W. K. Ma, F. Chinesta and M. R. Mackley. International Journal of Material Forming, 1(2), p. 89-99, 2008.[download pdf of draft] [link to the publisher]
  2. Improved domain tracking in meshless simulations of free-surface flows. A. Galavis, D. Gonzalez, I. Alfaro, E. Cueto. Computational Mechanics, vol. 42:467-479, 2008. [download pdf of draft]
  3. Real-time deformable models of non-linear tissues by model reduction techniques. S. Niroomandi, I. Alfaro, E. Cueto, F. Chinesta. Computer Methods and Programs in Biomedicine, Volume 91, Issue 3, 2008, Pages 223-231. [download pdf of draft]
  4. Higher-order Natural Element Methods: towards an isogeometric meshless method. David Gonzalez, Elias Cueto, Manuel Doblare. International Journal for Numerical Methods in Engineering, 74 (13), pp.1928-1954 , 2008. [download pdf of draft]

2007

  1. On Model Reduction and its Potential Applications in Chemical Enginneering. F. Chinesta, A. Ammar, E. Cueto. World Journal of Chemical Engineering, 1(1), pp. 3-12, 2007.
  2. A study on the performance of Natural Neighbour-based Galerkin Methods. I. Alfaro, J. Yvonnet, F. Chinesta and E. Cueto. International Journal for Numerical Methods in Engineering, 7(12), pp. 1436-1465, 2007. [download pdf of draft]
  3. A Natural Element updated Lagrangian approach for modelling Fluid-Structure interactions. A. Galavs, D. Gonzlez, E. Cueto, F. Chinesta, M. Doblare. European Journal of Computational Mechanics, 16(3-4), pp. 323-336, 2007.
  4. Natural Neighbour strategies for the simulation of laser surface coating processes. D. Gonzlez, D. Bel, E. Cueto, F. Chinesta, M. Doblare. International Journal of Forming Processes. Vol 10/1, pp.89-108, 2007. [download pdf of draft]

2006

  1. A Natural Neighbour Galerkin method with Octree structure. J. J. Laguardia, E. Cueto, M. Doblare. European Journal of Computational Mechanics, 15(5), pp. 529-548, 2006.
  2. A Natural Element updated Lagrangian strategy for free-surface Fluid Dynamics. D. Gonzalez, E. Cueto, F. Chinesta, M. Doblare. Journal of Computational Physics, 223(1), pp.127-150, 2007. [download pdf of draft]
  3. Nouvelles avances dans les methodes sans maillage de type elements naturels pour la simulation des procedes de mise en forme. J. Yvonnet, I. Alfaro, E. Cueto, F. Chinesta, P. Villon, M. Doblare. Revue Eurpeenne des Element Finis. 15/1-2-3, pp.29-40, 2006.
  4. On the a priori Model Reduction: Overview and recent developments. D. Ryckelynck, F. Chinesta, E. Cueto, A. Ammar. Archives of Computational Methods in Engineering, vol 13(1), pp. 91-128. 2006.
  5. Recent advances in the meshless simulation of aluminium extrusion and other related forming processes. I. Alfaro, D. Gonzalez, D. Bel, E. Cueto, M. Doblare, F. Chinesta. Archives of Computational Methods in Engineering, vol 13(1) pp. 3-44, 2006. [download pdf of draft]
  6. Three-dimensional simulation of aluminium extrusion by the alpha-shape based natural element method. I. Alfaro, D. Bel, E. Cueto, M. Doblare and F. Chinesta. Computer Methods in Applied Mechanics and Engineering, 195 (33-36), 2006. [download from Sciencedirect]
  7. Meshless methods with application to metal forming. I. Alfaro, J. Yvonnet, E. Cueto, F. Chinesta and M. Doblare. Computer Methods in Applied Mechanics and Engineering, Volume 195, Issues 48-49, Pages 6661-6675, 2006. [download from Sciencedirect]

2005

  1. A natural neighbour Galerkin method with quadtree structure. J. J. Laguardia, E. Cueto and M. Doblare. International Journal for Numerical Methods in Engineering, 63, 789-812, 2005. [download from IJNME]
  2. Alpha-NEM and model reduction: two new and powerful numerical techniques to describe flows involving short fibers suspensions. F. Chinesta, E. Cueto, D. Ryckelynck, A. Ammar. Revue europenne des lments finis, 14, 6-7, pp. 903–923, 2005. [download pdf of draft]
  3. On the employ of meshless methods in Biomechanics. M. Doblare, E. Cueto, B. Calvo, M. A. Martinez, J. M. Garcia, J. Cegoino. Computer Methods in Applied Mechanics and Engineering, 194, pp. 801-821, 2005.[download from sciencedirect]
  4. Meshless Methods and Partition of Unity Finite Elements. N. Sukumar, J. Dolbow, A. Devan, J. Yvonnet , F. Chinesta, D. Ryckelynck , P. Lorong , I. Alfaro, M. A. Martnez, E. Cueto, M. Doblare. International Journal of Forming Processes. 8(4), pp. 409–427, 2005. [download pdf of draft]

2004

  1. Induced anisotropy of foams forming processes: modelling and simulation. F. Chinesta, E. Cueto, P. Quintela and J. Paredes. Journal of Materials Processing Technology, vol. 155-156C, pp. 1482-1488, 2004. [download from sciencedirect]
  2. Volumetric locking in Natural Neighbour Galerkin methods. D. Gonzlez, E. Cueto and M. Doblare. International Journal for Numerical Methods in Engineering, vol. 61(4) pp. 611-632, 2004. [download from IJNME]
  3. Numerical integration in Natural Neighbour Galerkin methods. D. Gonzalez, E. Cueto, M. A. Martinez and M. Doblare. International Journal for Numerical Methods in Engineering, 60(12):2077-2104, 2004. [download from IJNME]
  4. Updated Lagrangian free surface flow simulations with Natural Neighbour Galerkin methods. M. A. Martinez, E. Cueto, I. Alfaro, M. Doblare, F. Chinesta. International Journal for Numerical Methods in Engineering, 60(13): 2105-2129, 2004. [download from IJNME]
  5. Thermomechanical cutting model discretisation: Eulerian or Lagrangian, mesh or meshless? F. Chinesta, Ph. Lorong, D. Ryckelynck, M. A. Martinez, E. Cueto, M. Doblare, G. Coffignal, M. Touratier, J. Yvonnet. International Journal of Forming Processes, vol. 7, (1-2) pp. 83-98, 2004.[download pdf of draft]

2003

  1. Overview and recent advances in Natural Neighbour Galerkin methods. E. Cueto, N. Sukumar, B. Calvo, M. A. Martinez, J. Cegoino and M. Doblare. Archives of Computational Methods in Engineering, Vol. 10 (4), pp:307-384, 2003. [download pdf of draft]
  2. Natural Element meshless simulation of flows involving short fiber suspensions. M. A. Martinez, E. Cueto, M. Doblare and F. Chinesta. Journal of non Newtonian Fluid Mechanics 115, pp. 51-78, 2003. [download from sciencedirect]
  3. On the imposition of essential boundary conditions in Natural Neighbor Galerkin Methods. E. Cueto, J. Cegoino, B. Calvo and M. Doblare. Communications in Numerical Methods in Engineering, vol 19 (5) pp 361-376, 2003.

2002

  1. Modelling three-dimensional piece-wise homogeneous domains using the alpha-shape based Natural Element Method. E. Cueto, B. Calvo and M. Doblare. International Journal for Numerical Methods in Engineering. Vol 54, issue 6, pp.871-897, 2002. [download from IJNME]

2001

  1. A meshless simulation of injection processes involving short fibers molten composites. M. A. Martinez, E. Cueto, M. Doblare and F. Chinesta. International Journal of Forming Processes. pp. 217-236. Vol 4, (3-4) Special Issue on Material Forming. A.M. Habraken, Ed. 2001.
  2. El mtodo de los Elementos Naturales en elasticidad compresible y cuasi incompresible. E. Cueto, M. A. Martinez and M. Doblare. Invited paper (in spanish), Boletin Tecnico del Instituto de Materiales y Modelos Estructurales. Universidad Central de Venezuela. Caracas. Venezuela. vol 39, n. 3, November 2001.

2000

  1. Impossing essential boundary conditions in the Natural Element Method by means of density-scaled alpha-shapes. E. Cueto, M. Doblare and L. Gracia. International Journal for Numerical Methods in Engineering, 49:519-546,2000. [download from IJNME]

If you are interested in any of these publications, feel free to send me a mail.

Elías Cueto