How Useful is Learning in Mitigating Mismatch Between Digital Twins and Physical Systems?
Journal article, 2022

In the control of complex systems, we observe two diametrical trends: model-based control derived from digital twins, and model-free control through AI. There are also attempts to bridge the gap between the two by incorporating learning-based AI algorithms into digital twins to mitigate mismatches between the digital twin model and the physical system. One of the most straightforward approaches to this is direct input adaptation. In this paper, we ask whether it is useful to employ a generic learning algorithm in such a setting, and our conclusion is "not very". We denote an algorithm to be more useful than another algorithm based on three aspects: 1) it requires fewer data samples to reach a desired minimal performance, 2) it achieves better performance for a reasonable number of data samples, and 3) it accumulates less regret. In our evaluation, we randomly sample problems from an industrially relevant geometry assurance context and measure the aforementioned performance indicators of 16 different algorithms. Our conclusion is that blackbox optimization algorithms, designed to leverage specific properties of the problem, generally perform better than generic learning algorithms, once again finding that "there is no free lunch".

Smart manufacturing

blackbox optimization

cyber-physical systems

learning (artificial intelligence)

adaptive optimization

digital twins


Constantin Cronrath

Chalmers, Electrical Engineering, Systems and control

Bengt Lennartson

Chalmers, Electrical Engineering, Systems and control

IEEE Transactions on Automation Science and Engineering

1545-5955 (ISSN)

Vol. In Press

Smart Assembly 4.0

Swedish Foundation for Strategic Research (SSF) (RIT15-0025), 2016-05-01 -- 2021-06-30.


VINNOVA (2020-01947), 2020-10-01 -- 2023-09-30.

Subject Categories

Production Engineering, Human Work Science and Ergonomics


Control Engineering

Areas of Advance




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