Digital twins for decisions

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Full title: Enablingg technologies for predictive digital twins for decision supportin

Speaker: Trond Kvamsdal 

ABSTRACT:

We adopt the following definition of a Digital Twin [1]: A digital twin is defined as a virtual representation of a physical asset, or a process enabled through data and simulators for real-time prediction, optimization, monitoring, control, and decision-making.

To enable predictive twins, we utilize Hybrid Analysis and Modelling (HAM) [2-5] that combines classical Physic-Based Methods (PBM) accelerated by means of Reduced Order Modelling (ROM) [6-8] together with Data-Driven Methods (DDM) based on sensor measurement analysed by use of Machine Learning (ML). Furthermore, we do Optimal Sensor Placement (OSP) based on ROM and Sparse Sensing.

We will present different ROM and HAM techniques applicable for decision support applied to structural and fluid mechanics problems in this talk. 

REFERENCES:

[1]     A. Rasheed, O. San, T. Kvamsdal. Digital Twin: Values, Challenges and Enablers from a Modeling Perspective. IEEE Access, 8:21980--22012, 2020, 2020.

[2]      O. San, A. Rasheed, T. Kvamsdal. Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution. GAMM‐Mitteilungen, e202100007. 2021.

[3]      S. Pawar, O. San, B. Aksoylu, A. Rasheed, T. Kvamsdal. Physics guided machine learning using simplified theories. Physics of Fluids, 33, 011701, 2021.

[4]     S. S. Blakseth, A. Rasheed, T. Kvamsdal, O. San: Combining physics-based and data-driven techniques for reliable Hybrid Analysis and Modeling using the corrective source term approach. Applied Soft Computing, 128, 109533, 2022.

[5]      S. S. Blakseth, A. Rasheed, T. Kvamsdal, O. San. Deep neural network enabled corrective source term approach to hybrid analysis and modelling. Neural Networks, 146, 181-199, 2022.

[6]      E. Fonn, H. van Brummelen, T. Kvamsdal, and A. Rasheed. Fast divergence-conforming reduced basis methods for steady Navier-Stokes flow. Computer Methods in Applied Mechanics and Engineering, 346:486-512, 2019.

[7]      V. Tsiolakis, T. Kvamsdal, A. Rasheed, E. Fonn and H. van Brummelen. Reduced order models for finite-volume simulations of turbulent flow around wind-turbine blades. Journal of Physics: Conference Series, Volume 2018, 012042, 2021.

[8]      E. Fonn, H. van Brummelen, J. Lohne Eftang, T. Rusten, K. A. Johannessen, T. Kvamsdald, A. Rasheed. Least-squares projected models for non-intrusive affinization of Reduced Basis Methods. International Journal of Numerical Methods in Engineering, 126, no. 18 (2025): e70127, https://doi.org/10.1002/nme.70127.