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)
- 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, accepted for publication, 2019. Download PDF of draft, documentary video]
- Learning slosh dynamics by means of data. B. Moya, D. Gonzalez, I. Alfaro, F. Chinesta, E. Cueto. Computational Mechanics, accepted for publication, 2019. [Download PDF of draft]
- 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, accepted for publication, 2019. [Download PDF of draft]
- 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, in press, 2018. [Download PDF of draft]
- 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, in press, 2018. [Download PDF of draft]
- 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]
- 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)]
Some documentary videos
Project OTRI 2019-0060 financed by ESI Group