Hybrid twins for the effective monitoring of real-life engineering systems

Date d'évènement : 20/02/2024

Ludovic Chamoin / LMPS, ENS Paris-Saclay

Hybrid twins for the effective monitoring of real-life engineering systems

Abstract : The real-time monitoring of real-life engineering systems, by coupling simulation tools and observed data (Dynamic Data Driven Application Systems – DDDAS), is made very difficult in practice due to several issues. In particular, the complex nonlinear multiscale phenomena which are involved may be associated with computationally intensive simulations (hardly compatible with real-time) which requires reduced order modeling and/or model coarsening. In addition, the problem is plagued with model bias, uncertain environment, and measurement noise, which need to be taken into account for robust data assimilation, accurate diagnosis and prognosis, and safe decision-making. An appealing trend is to refer to hybrid techniques, in which an a priori physics-guided model is updated and enriched with data-based information, thus making benefit of all knowledge available.

In this context, we present some effective methods and recent developments for the construction of such hybrid twins, with practical applications on the online monitoring of engineering systems (e.g., control of additive manufacturing processes, or structural health monitoring) with sequential assimilation of real data. We also give some insights on the beneficial use of deep learning in such hybrid strategies.

  1. W. Haik, Y. Maday, L. Chamoin, A real-time variational data assimilation method with data-driven enrichment for time-dependent problems, Computer Methods in Applied Mechanics and Engineering, 405 :115868 (2023)
  2. M. Diaz, P-E. Charbonnel, L. Chamoin, A new Kalman filter approach for structural parameter tracking : application to the monitoring of damaging structures tested on shaking tables, Mechanical Systems and Signal Processing, 182 :109529 (2023)
  3. M. Diaz, P-E. Charbonnel, L. Chamoin, Merging experimental design and structural identification around the concept of modified Constitutive Relation Error in low-frequency dynamics for enhanced structural monitoring, Mechanical Systems and Signal Processing, 197 :110371 (2023)
  4. A. Benady, E. Baranger, L. Chamoin, NN-mCRE: A modified Constitutive Relation Error framework for unsupervised learning of nonlinear state laws with physics-augmented neural networks, International Journal for Numerical Methods in Engineering, In press §2024)
  5. W. Haik, L. Chamoin, Y. Maday, Real-time thermal monitoring in additive manufacturing processes from in-situ sensing, reduced order modeling and model bias correction, submitted (2023)
  6. S. Farahbakhsh, L. Chamoin, M. Poncelet, Continuous structural health monitoring with a modified Kalman filter applied on optic fiber sensing data, submitted (2024)
  7. S. Massala, L. Chamoin, M. Pica Ciamarra, Hybrid twins using PBDW and neural operator for effective state estimation on complex systems, submitted (2024)

Le mardi 20 février 2024 à 11h00 / Amphithéâtre François Canac, LMA

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