CrediblE project

Data assimilation for credible engineering simulations

This page displays information about the project CICYT-DPI2017-85139-C2-1-R, funded by the Spanish Ministry of Economy and Innovation. Principal Investigator of the project: Elias Cueto

Participants:

Group of Applied Mechanics and Bioengineering. amb-I3A.

Elias Cueto

Iciar Alfaro

David Gonzalez

Francisco Chinesta (ENSAM ParisTech)

Laboratori de Calcul Numeric, UPC-BarcelonaTech. Lacan.

Antonio Huerta, PI of the Lacan sub-project.

Pedro Diez

Sergio Zlotnik

Alberto García

Swansea University, College of Engineering

Rubén Sevilla

Goals of the project:

Credibility has been defined as a sufficient degree of belief in the validity of a model to justify its use for research and decision making (Rykiel, 1996). In other words, as the satisfaction of the end-user in terms of accuracy, robustness, and uncertainty of the simulation. In the context of data-intensive science and technology (industry 4.0, Internet of Things, …) industrialists need credible simulation procedures embedded into decision making protocols.

Data assimilation (the process by which experimental observations of the system are incorporated into the model) appears therefore as a key ingredient in this process. The purpose of this project is to develop novel computational techniques to simulate industrial and medical problems with two critical aspects for the user. On one hand, it is essential to dynamically incorporate observations of the system into the decision-making pipeline. On the other hand, it is crucial for the end-user to incorporate state-of-the art simulations with quantifiable credibility indicators into the daily decision-making routine. The ultimate goal is to put forward the basis for a new generation of numerical tools for the connected industry and autonomous devices such as cars.

To achieve this, CrediblE is focused on three specific industrial challenges (IPs): radar sensor modelling for autonomous cars, augmented reality for laparoscopic surgery assistance and manufacturing in the connected industry. These three problems, although apparently unconnected, share many methodological similarities. They require fast and reliable modern techniques based on model order reduction, manifold (machine) learning or artificial intelligence, just to name a few. Moreover, all need incorporating observations (data assimilation). Finally, the credible simulation for a decision-making process needs accuracy assessment, robustness & sensitivity, as well as uncertainty quantification (UQ). Note that UQ also includes other common ingredients of the problems at hand: the need for fast (real-time) solution of inverse problems arising from the experimental observations, parameter estimation and data assimilation.

Therefore, CrediblE aims at providing tools to face the Social Challenge on Economy and Digital Society (#7 from the list of those identified by the Spanish RTD Strategy). Incidentally, making progress in the three IPs (autonomous cars, surgery and manufacturing) is having impact in three other Social Challenges: Health, Demographical Change and Welfare (#1); Clean, Safe and Sustainable Energy (#3) and Integrated and Sustainable Smart Transport (#4).

Some documentary simulations:

Project’s YouTube channel

Publications

FORTHCOMING

  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. Submitted, 2019.
  2. 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, accepted for publication, 2019. [Download PDF of draft]
  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, accepted for publication, 2019. [Download PDF of draft]
  4. 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]

2019

  1. A local, multiple 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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].
  7. 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]
  8. 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. 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]
  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]