Simulated Reality

An intelligence augmentation system based on Hybrid Twins and Augmented Reality

We think that intelligence augmentation systems should be based on a type of artificial intelligence that goes beyond pattern recognition (i.e., deep learning, …)

Instead, we propose to develop systems that build causal models of the engineering systems of interest, able to provide explanation and understanding to the physical phenomena taking place.

To achieve this ambitious goal, we propose to employ different ingredients:

1. Hybrid Twins. Since the developed system will obtain information from the surrounding environment through computer vision, it will posses valuable information (data) to assimilate, but also possibly to correct those models that may be incorrect or incomplete. This fits into ESI’s vision of what a Hybrid Twin is: a dynamic, data-driven twin that is both physical and digital.

2. Real-time simulation. For this to be possible, one bottleneck is our ability to perform high-fidelity simulation in (or faster than) real time. This will be possible by employing model order reduction techniques and, particularly, by exploiting our vast knowledge on Proper Generalized Decompositions, acquired through our longstanding collaboration with prof. Chinesta and his group.

3. Augmented reality. Finally, information arising from the embedded models and the acquired data will be displayed by means of augmented reality devices (tablet, hololens glasses, …)


Elías Cueto (PI)

David González 

Icíar Alfaro

Alberto Badías

Beatriz Moya


  1. Physics perception in sloshing scenes with guaranteed thermodynamic consistency. B. Moya, A. Badias, D. Gonzalez, F. Chinesta, E. Cueto. Submitted, 2021. [Project video] [arXiv preprint]
  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, accepted for publication, 2021. [arXiv preprint]
  3. 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, accepted for publication, 2021. [Download from arXiv]
  4. PGD-based advanced nonlinear multiparametric regressions for constructing metamodels at the scarce-data limit. Abel Sancarlos, Victor Champaney, Jean-Louis Duval, Elias Cueto, Francisco Chinesta. Submitted. [Download from Arxiv]
  5. MORPH-DSLAM: Model Order Reduction for PHysics-based Deformable SLAM. A. Badias, I. Alfaro, D. Gonzalez, F. Chinesta, E. Cueto. Accepted for publication, Transactions on Pattern Analysis and Machine Intelligence, 2021. [arXiv preprint] [video]
  6. Digital twins that learn and correct themselves. Beatriz Moya, Alberto Badías, Icíar Alfaro, Francisco Chinesta, Elías Cueto. International Journal for Numerical Methods in Engineering, accepted for publication, 2020. [Download PDF of draft]
  7. 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)]
  8. 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].
  9. 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]
  10. 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, 14pages 83–95 (2021). [Link to publisher]
  11. 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]
  12. 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]
  13. 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)]
  14. 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)].
  15. 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]
  16. Learning non-Markovian physics from data. D. Gonzalez, F. Chinesta, E. Cueto. Journal of Computational Physics, Volume 428, 109982. 2021. [Download PDF of draft]
  17. 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]
  18. 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]
  19. 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.
  20. 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, 12117, 3849-3868, 2020. [Download PDF of draft]
  21. 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]
  22. 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]
  23. 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]
  24. 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]

Some documentary videos

Project OTRI 2019-0060 financed by ESI Group