Skip to main content

On a Physics-Compatible Approach for Data-Driven Computational Mechanics

  • Chapter
  • First Online:
Current Trends and Open Problems in Computational Mechanics

Abstract

Within the framework of the thermodynamics of irreversible processes, one introduces a general vision of data-driven computational mechanics, adapted to history-dependent materials, through the concept of the “Experimental Constitutive Manifold” (ECM). The mathematical structure of the ECM, which involves internal state variables, constitutes the material model associated with the available experimental data. The hidden variables are not known a priori but are calculated from the experimental data thanks to the so-called “ECM-Central Problem”. This paper also tries to present recent advances in the data-driven computation approach. The potential applications are illustrated through the proposed new way to describe mathematically the material, where a priori assumptions on modelling are absent.

This paper is dedicated to Peter Wriggers for his 70th birthday. I first met Peter a long time ago and, frankly, I don’t remember when our friendship began. Peter is not only an internationally renowned scientist, but also a warm-hearted person who respects and cares for others. We have participated together in many conferences, committees and a number of courses and, most recently, in the IRTG doctoral student exchange program between the University of Hanover and ENS Paris-Saclay directed by him and Olivier Allix. We had a great time together, not only at work. I remember in particular a wine tasting in Udine with also Erwin Stein and Rolf Rannacher and also exceptional evenings with our wives in great restaurants in Paris.

Pierre Ladevèze.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kirchdoerfer, T., & Ortiz, M. (2016). Data-driven computational mechanics. Computer Methods in Applied Mechanics and Engineering, 304, 81–101.

    Article  MathSciNet  Google Scholar 

  2. Chinesta, F., Ladeveze, P., Ibanez, R., Aguado, J. V., Abisset-Chavanne, E., & Cueto, E. (2017). Data-driven computational plasticity. Procedia Engineering, 207, 209–214.

    Article  Google Scholar 

  3. Ibañez, R., Borzacchiello, D., Aguado, J. V., Abisset-Chavanne, E., Cueto, E., Ladeveze, P., & Chinesta, F. (2017). Data-driven non-linear elasticity: Constitutive manifold construction and problem discretization. Computational Mechanics, 60(5), 813–826.

    Article  MathSciNet  Google Scholar 

  4. Eggersmann, R., Kirchdoerfer, T., Reese, S., Stainier, L., & Ortiz, M. (2019). Model-free data-driven inelasticity. Computer Methods in Applied Mechanics and Engineering, 350, 81–99.

    Article  MathSciNet  Google Scholar 

  5. González, D., Chinesta, F., & Cueto, E. (2019). Thermodynamically consistent data-driven computational mechanics. Continuum Mechanics and Thermodynamics, 31(1), 239–253.

    Article  MathSciNet  Google Scholar 

  6. Guo, M., & Hesthaven, J. S. (2019). Data-driven reduced order modeling for time-dependent problems. Computer Methods in Applied Mechanics and Engineering, 345, 75–99.

    Article  MathSciNet  Google Scholar 

  7. Ibáñez, R., Abisset-Chavanne, E., González, D., Duval, J. L., Cueto, E., & Chinesta, F. (2019). Hybrid constitutive modeling: Data-driven learning of corrections to plasticity models. International Journal of Material Forming, 12(4), 717–725.

    Article  Google Scholar 

  8. Ladevèze, P., Néron, D., & Gerbaud, P. W. (2019). Data-driven computation for history-dependent materials. Comptes Rendus de l’Academie des Sciences. Mécanique, 347(11):831–844.

    Google Scholar 

  9. Lopez, E., Gonzalez, D., Aguado, J. V., Abisset-Chavanne, E., Cueto, E., Binetruy, C., & Chinesta, F. (2018). A manifold learning approach for integrated computational materials engineering. Archives of Computational Methods in Engineering, 25(1), 59–68.

    Article  MathSciNet  Google Scholar 

  10. Leygue, A., Coret, M., Réthoré, J., Stainier, L., & Verron, E. (2018). Data-based derivation of material response. Computer Methods in Applied Mechanics and Engineering, 331, 184–196.

    Article  MathSciNet  Google Scholar 

  11. Liu, Z., Bessa, M. A., & Liu, W. K. (2016). Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials. Computer Methods in Applied Mechanics and Engineering, 306, 319–341.

    Article  MathSciNet  Google Scholar 

  12. Versino, D., Tonda, A., & Bronkhorst, C. A. (2017). Data driven modeling of plastic deformation. Computer Methods in Applied Mechanics and Engineering, 318, 981–1004.

    Article  Google Scholar 

  13. Gerbaud, P. -W., Ladevèze, P., & Néron, D. (to appear) Solving a fundamental problem at the core of a data-driven approach for history-dependent materials.

    Google Scholar 

  14. Ladevèze, P. (2020). Le calcul piloté par les données pour les matériaux à mémoire : théorie, pratique, application. LMT Paris-Saclay: Technical Report.

    Google Scholar 

  15. Ladevèze, P., Gerbaud, P. -W., & Néron, D. (to appear). A thermodynamics-compatible approach to data-driven computation.

    Google Scholar 

  16. Ladevèze, P. (1989). The large time increment method for the analyse of structures with nonlinear constitutive relation described by internal variables. Comptes rendus de l’Academie des sciences. Série 2, 309(2):1095–1099.

    Google Scholar 

  17. Ladevèze, P. (1999). Nonlinear computational structural mechanics: New approaches and non-incremental methods of calculation. New York: Springer.

    Book  Google Scholar 

  18. Néron, D., & Ladevèze, P. (2010). Proper generalized decomposition for multiscale and multiphysics problems. Archives of Computational Methods in Engineering, 17(4), 351–372.

    Article  MathSciNet  Google Scholar 

  19. Ladeveze, P., & Leguillon, D. (1983). Error estimate procedure in the finite element method and applications. SIAM Journal on Numerical Analysis, 20(3), 485–509.

    Article  MathSciNet  Google Scholar 

  20. Ladevèze, P., & Chouaki, A. (1999). Application of a posteriori error estimation for structural model updating. Inverse Problems, 15(1), 49–58.

    Article  MathSciNet  Google Scholar 

  21. Ladevèze, P., & Pelle, J.-P. (2005). Mastering calculations in linear and nonlinear mechanics. New York: Springer.

    MATH  Google Scholar 

  22. A. García-González, A. Huerta, S. Zlotnik, and P. Díez. A kernel Principal Component Analysis (kPCA) digest with a new backward mapping (pre-image reconstruction) strategy. arxiv.org, (2008):1–16, 2020.

  23. M. H. Nguyen and F. D. la Torre Frade. Robust Kernel Principal Component Analysis. In Proceedings of (NeurIPS) Neural Information Processing Systems, dec 2008.

    Google Scholar 

  24. F. Hild, A. Bouterf, L. Chamoin, H. Leclerc, F. Mathieu, J. Neggers, F. Pled, Z. Tomičević, and S. Roux. Toward 4D mechanical correlation. Advanced Modeling and Simulation in Engineering Sciences, 3(1), 2016.

    Google Scholar 

  25. Neggers, J., Allix, O., Hild, F., & Roux, S. (2018). Big Data in Experimental Mechanics and Model Order Reduction: Today’s Challenges and Tomorrow’s Opportunities. Archives of Computational Methods in Engineering, 25(1), 143–164.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pierre Ladevèze .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ladevèze, P., Gerbaud, PW., Néron, D. (2022). On a Physics-Compatible Approach for Data-Driven Computational Mechanics. In: Aldakheel, F., Hudobivnik, B., Soleimani, M., Wessels, H., Weißenfels, C., Marino, M. (eds) Current Trends and Open Problems in Computational Mechanics. Springer, Cham. https://doi.org/10.1007/978-3-030-87312-7_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87312-7_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87311-0

  • Online ISBN: 978-3-030-87312-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics