We present a #digitaltwin for fluid sloshing that, through computer vision and making an extensive use of simulation in the background, is able to learn the particular fluid and its behavior—even if the robot has not been exposed to it before—and predict its dynamics in the near future.
The last number of SIAM News presents an article about data-enabled predictive modeling which covers some of our works in the field of data-driven computational mechanics. You can find it here: https://sinews.siam.org/Details-Page/data-enabled-physics-constrained-predictive-modeling-of-complex-systems
Our paper on local-PGD methods has just been accepted for publication at International Journal for Numerical Methods in Engineering. You can download it at http://amb.unizar.es/PDFs/l-PGD.pdf
Three of our group’s papers are among the 1% most cited papers of their respective specialities.
These papers are:
- 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]
- 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]
- 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]
They are also among the few papers from our university in this list.
Our paper “A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity” has just been accepted for publication in Archives of Computational Methods in Engineering.
You can download the draft [here].