Internet of Robotic Things

Digital and Hybrid Twins

The symbiosis of IoT and robotics is creating the field of Internet of Robotic Things (IoRT), where a robot is the one who processes, analyzes and automates decisions.

In our group we develop techniques of scientific machine learning that allow us to construct digital twins able to correct themselves from data.


  1. Numerical experiments on manifold learning, manifolds dimension and latent variables. Ruben Ibanez, Pierre Gilormini, Elias Cueto and Francisco Chinesta. Submitted, 2020.
  2. Structure-preserving neural networks. Q. Hernandez, A. Badias, D. Gonzalez, F. Chinesta, E. Cueto. Submitted, 2020. [Arxiv preprint]
  3. Physically sound, self-learning digital twins for sloshing fluids. B. Moya, I. Alfaro, D. Gonzalez, F. Chinesta, E. Cueto. Submitted, 2020.
  4. Learning non-Markovian physics from data. D. Gonzalez, F. Chinesta, E. Cueto. Submitted, 2019.
  5. Digital twins that learn and correct themselves. Beatriz Moya, Alberto Badías, Icíar Alfaro, Francisco Chinesta, Elías Cueto. Submitted, 2019.
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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)]