InSilico project

In Silico simulation-based engineering for real-time decision making


This page displays information about the project CICYT-DPI2014-51844-C2-1-R, funded by the Spanish Ministry of Economy and Innovation. Scientific leader of the project: Elias Cueto


Group of Applied Mechanics and Bioengineering. amb-I3A.

Elias Cueto

Iciar Alfaro

David Bel

David Gonzalez

Francisco Chinesta (GeM EC-Nantes)

Laboratori de Calcul Numeric, UPC-BarcelonaTech. Lacan.

Antonio Huerta, PI of the Lacan sub-project.

Pedro Diez

Sergio Zlotnik

Nuria Pares

Goals of the project:

The capital aim of this project is to develop novel computational models and numerical methods to genuinely and drastically advance simulation-based decision-making for cutting-edge industrial problems. It will confront existing knowledge gaps on fundamental approaches in reduced order model techniques for real-time, inverse and optimization problems, and it will deliver comprehensive understanding and tools for simulation in problems seen by industry as a major asset to increase performance and competitiveness. The ultimate goal is to set the basis for the next generation of simulation tools that will be used for industry related problems within Europe. In spite the international efforts in this area, these objectives represent a challenge beyond today’s state-of-the-art both from the fundamental computational engineering/science perspective and from the applied engineering industrial standpoint. To achieve these objectives, InSilico is focused on three specific industrial problems (IP), namely, fast simulation supporting surgery; electric grids, including uncertainty quantification and decision making; and aerodynamical and mechanical simulation for shape design and optimization in the automotive industry. The project 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). The instruments for decision making provided by the methologies developed here are to be implemented in portable digital devices. The possibility of producing online real-time simulations with the computational capacity of a smart phone is creating a new paradigm in the social use of the simulation software for industrial design, for management and control of complex systems and for monitoring health technologies. Incidentally, making progress in the three IPs (Surgery, Electric grids and Automotion) 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). These three big problems, apparently dissimilar and unrelated, share one essential feature: the presence of many queries and the need for real-time response. By real time different things are meant depending on the context, but in general this relates to the need of a fast and reliable response provided in times much lower than those usual for everyday engineering simulation practice, and ranging from some milliseconds in virtual surgery to some minutes in shape optimisation. To efficiently solve these problems, it is envisaged that model order reduction techniques constitute nowadays the sole alternative to more traditional tools that, despite the generalization of supercomputing infrastructures, have not provided a satisfactory solution yet under these astringent requirements. Among the plethora of possible model order reduction techniques, Proper Generalized Decomposition techniques have demonstrated, despite their youth, an impressive capacity to provide real time response in a wide variety of problems. By stating the problem at hand into a parametric formulation, a sort of computational vademecum is obtained, that allows for real-time post-processing rather than real-time simulation. This computational vademecum approach opens unprecedented possibilities for real time and many query problems, as previous works of the applicants have already demonstrated.

Core objectives:

  1. Fast simulation for medical support
  2. Fast simulation for geometrical design
  3. Fast simulation for smart grid design

Some documentary simulations:

Project’s YouTube channel



  1. Local Proper Generalized Decomposition. A. Badías, D. González, I. Alfaro, F. Chinesta, E. Cueto. International Journal for Numerical Methods in Engineering, 112:12,1715–1732, 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]
  3. A Physically-Based Fractional Diffusion Model for Semi-Dilute Suspensions of Rods in a Newtonian Fluid. E. Nadal, J. V. Aguado, E. Abisset, R. Keunings, E. Cueto, F. Chinesta. Applied Mathematical Modelling, 51, 58-67, 2017. [Download pdf of draft].
  4. 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]


  1. Computational vademecums for real-time simulation of surgical cutting in haptic environments. C. Quesada, D. Gonzalez, I. Alfaro, E. Cueto and F. Chinesta. International Journal for Numerical Methods in Engineering. 108 (10), 1230-1247, 2016. [Download pdf of draft]
  2. Vademecum-based GFEM (V-GFEM): Optimal Enrichment for transient problems. D. Canales, A. Leygue, F. Chinesta, D. Gonzalez, E. Cueto, E. Feulvarch, J.-M. Bergheau, A. Huerta. International Journal for Numerical Methods in Engineering, 108(9), 971-989, 2016. [Download pdf of draft]
  3. Real-time simulation techniques for augmented learning in science and engineering. C. Quesada, D. Gonzalez, I. Alfaro, E. Cueto, A. Huerta and F. Chinesta. The Visual Computer, 32(11), 1465-1479, 2016. [Download pdf of draft]
  4. In-plane-out-of-plane separated representations of updated-Lagrangian descriptions of thermomechanical models defined in plate domains. D. Canales, A. Leygue, F. Chinesta, I. Alfaro, D. Gonzalez, E. Cueto, E. Feulvarch and J.M. Bergheau. Comptes Rendus Mecanique, 344, 4-5, 225-235, 2016. [Download pdf of draft]
  5. Chemical Master Equation Empirical Moment Closure. A. Ammar, M. Magnin, O. Roux, E. Cueto, F. Chinesta. Biological Sciences open access, 5:155. doi:10.4172/2329-6577.1000155, 2016. [Download pdf of draft]
  6. On the use of model order reduction for simulating automated fibre placement processes. Nicolas Bur, Pierre Joyot, Chady Ghnatios, Pierre Villon, Elias Cueto, Francisco Chinesta. Advanced Modeling and Simulation in Engineering Sciences (AMSES), 3:4, 2016. [Download pdf of paper (OA)]
  7. Computational patient avatars for surgery planning. D. Gonzalez, E. Cueto and F. Chinesta. Annals of Biomedical Engineering, 44(1), 35-45. 2016. [Download pdf of draft]


  1. Effect of the separated approximation of input data in the accuracy of the resulting PGD solution. Sergio Zlotnik, Pedro Diez, Elias Cueto, David Gonzalez and Antonio Huerta. Advanced Modeling and Simulation in Engineering Sciences (AMSES), 2:28, 2015. [Download pdf of draft]
  2. Towards a pancreatic surgery simulator based on model order reduction. Andres Mena, David Bel, Iciar Alfaro, David Gonzalez, Elias Cueto, and Francisco Chinesta. Advanced Modeling and Simulation in Engineering Sciences (AMSES), 2:31, 2015. [Download pdf of draft]
  3. An error estimator for real-time simulators based on model order reduction. Iciar Alfaro, David Gonzalez, Sergio Zlotnik, Pedro Diez, Elias Cueto, and Francisco Chinesta. Advanced Modeling and Simulation in Engineering Sciences (AMSES), 2:30, 2015. [Download pdf of draft]
  4. Fast and reliable gate arrangement pre-design of resin infusion processes. F. Sanchez, L. Domenech, V. Garcia, N. Montes, A. Falco, E. Cueto, F. Chinesta, P. Fideu. Composites A, 77, p. 285-292, 2015. [Download pdf of draft]
  5. A Second-Gradient Theory of Dilute Suspensions of Flexible Rods in a Newtonian Fluid. E. Abisset-Chavanne, J. Ferec, G. Aussias, E. Cueto, F. Chinesta, R. Keunings. Archives of Computational Methods in Engineering, 22, 511-527, 2015. [Download pdf of draft]
  6. Efficient stabilization of advection terms involved in separated representations of Boltzmann and Fokker-Planck equations. F. Chinesta, E. Abisset-Chavanne, A. Ammar, E. Cueto. Communications in Computational Physics, 17 (4), pp. 975-1006, 2015. [Download pdf of draft]
  7. Real-time monitoring of thermal processes by reduced order modeling. Jose V. Aguado, Antonio Huerta, Francisco Chinesta and Elias Cueto. International Journal for Numerical Methods in Engineering, 102(5), 991-1017, 2015. [Download pdf of draft]
  8. Kinetic Theory Modeling and Efficient Numerical Simulation of Gene Regulatory Networks Based on Qualitative Descriptions. Francisco Chinesta, Morgan Magnin, Olivier Roux, Amine Ammar, Elias Cueto, Entropy 2015, 17(4), 1896-1915; doi:10.3390/e17041896. [Download Open Access PDF]
  9. Meshless methods for the simulation of material forming. A review. E. Cueto, F. Chinesta. International Journal of Material Forming, 8(1), 25-43, 2015. [Download pdf of draft]
  10. Fractional modeling of functionalized CNT suspensions. J.V. Aguado, E. Abisset-Chavanne, E. Cueto, F. Chinesta, R. Keunings. Rheologica Acta, 54, 109-119, 2015. [Download pdf of draft]
  11. Computational vademecums for the real-time simulation of haptic collision between nonlinear solids. D. Gonzalez, I. Alfaro, C. Quesada, E. Cueto, F. Chinesta. Computer Methods in Applied Mechanics and Engineering, 283 (2015) 210–223. [Download pdf of draft]