Digital twins that learn and correct themselves
Article dans une revue avec comité de lecture
Abstract
Digital twins can be defined as digital representations of physical entities that employ real-time data to enable understanding of the operating conditions of these entities. Here we present a particular type of digital twin that involves a combination of computer vision, scientific machine learning, and augmented reality. This novel digital twin is able, therefore, to see, to interpret what it sees—and, if necessary, to correct the model it is equipped with—and presents the resulting information in the form of augmented reality. The computer vision capabilities allow the twin to receive data continuously. As any other digital twin, it is equipped with one or more models so as to assimilate data. However, if persistent deviations from the predicted values are found, the proposed methodology is able to correct on the fly the existing models, so as to accommodate them to the measured reality. Finally, the suggested methodology is completed with augmented reality capabilities so as to render a completely new type of digital twin. These concepts are tested against a proof-of-concept model consisting on a nonlinear, hyperelastic beam subjected to moving loads whose exact position is to be determined.
Files in this item
Related items
Showing items related by title, author, creator and subject.
-
Article dans une revue avec comité de lectureMOYA, Beatriz; ALFARO, Iciar; GONZALEZ, David; CHINESTA, Francisco; CUETO, Elías (Public Library of Science, 2020)In this paper, a novel self-learning digital twin strategy is developed for fluid sloshing phenomena. This class of problems is of utmost importance for robotic manipulation of fluids, for instance, or, in general, in ...
-
Article dans une revue avec comité de lectureMOYA, Beatriz; GONZÁLEZ, David; ALFARO, Iciar; CHINESTA, Francisco; CUETO, Elías G. (Springer Verlag, 2019)In this work we study several learning strategies for fluid sloshing problems based on data. In essence, a reduced-order model of the dynamics of the free surface motion of the fluid is developed under rigorous thermodynamics ...
-
Article dans une revue avec comité de lectureBADIAS, Alberto; ALFARO, Iciar; GONZALEZ, David; CHINESTA, Francisco; CUETO, Elias (Elsevier, 2018)In this work we explore the possibilities of reduced order modeling for augmented reality applications. We consider parametric reduced order models based upon separate (affine) parametric dependence so as to speedup the ...
-
Article dans une revue avec comité de lectureBADÍAS, Alberto; GONZÁLEZ, David; ALFARO, Iciar; CHINESTA, Francisco; CUETO, Elías (Wiley, 2020)We present a real-time method for computing the mechanical interaction between real and virtual objects in an augmented reality environment. Using model order reduction methods we are able to estimate the physical behavior ...
-
Article dans une revue avec comité de lectureBADÍAS, Alberto; CURTIT, Sarah; GONZÁLEZ, David; ALFARO, Iciar; CHINESTA, Francisco; CUETO, Elías G. (Wiley, 2019)While modern CFD tools are able to provide the user with reliable and accurate simulations, there is a strong need for interactive design and analysis tools. State-of-the-art CFD software employs massive resources in terms ...