Digital twins that learn and correct themselves

Moya, Beatriz; Badías, Alberto; Alfaro, Icíar; Chinesta, Francisco

doi:10.1002/nme.6535

Cited by 39 publications

(27 citation statements)

References 44 publications

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“…The digital twin technology integrates the data, models, calculation logic, simulation, and sensor feedback of the physical world and the virtual world to draw a complete twin computing system and twin life mechanism [58,59]. We map the physical space and virtual space of creative choreography to three choreography stages: creative stage, rehearsal stage, and live stage, and propose a computable framework for PCC based on digital twins (see Figure 5).…”

Section: Twin Sensor Network (Tsn)mentioning

confidence: 99%

TSN: Performance Creative Choreography Based on Twin Sensor Network

Zhang

Ding

et al. 2021

Wireless Communications and Mobile Computing

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The purpose of this paper is to improve the efficiency of performance creative choreography (PCC). Our research work shows that we can realize the model integration and data optimization for PCC in complex environments based on the combined architecture of sensor network (SN) and machine-learning algorithm (MLA). In order to explain the process and content of this research better, this paper designs a specific problem description framework for PCC, which mainly includes the following content: (1) a twin sensor network (TSN) architecture based on digital twin information interaction is proposed, which defines and describes the acquisition method, classification (creative data, rehearsal data, and live data), and temporal and spatial features of performance data. (2) Proposed a mobile computing method based on director semantic annotation (DSA) as the core computing module of TSN. (3) A spatial dynamic line (SDL) model and a creative activation mechanism (CAM) based on DSA are proposed to realize fast and efficient PCC of dance with the TSN architecture. Experimental results show that the TSN architecture proposed in this article is reasonable and effective. The SDL model achieved significantly better performance with little time increase and improved the computability and aesthetics of PCC. New research ideas are proposed to solve the computational problem of PCC in complex environments.

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Section: Twin Sensor Network (Tsn)mentioning

confidence: 99%

TSN: Performance Creative Choreography Based on Twin Sensor Network

Zhang

Ding

et al. 2021

Wireless Communications and Mobile Computing

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“…However, due to the purely data driven approach, a vast number of computationally expensive simulation solutions are required for sufficient training of the surrogate model, which could create challenges for accuracy and generalization when switching to an experimental data source for training. Purely data-driven approaches can be beneficial for those problems where few relationships are identified, as they can help to detect hidden relationships in data; however, when established physical-laws apply and available data is scarse or biased, the utilization of physically-related data-driven approaches can be countervailing and utile [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Modelling by Machine Learning Corrections of Analytical Model Predictions towards High-Fidelity Simulation Solutions

et al. 2021

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Within the fields of materials mechanics, the consideration of physical laws in machine learning predictions besides the use of data can enable low prediction errors and robustness as opposed to predictions only based on data. On the one hand, exclusive utilization of fundamental physical relationships might show significant deviations in their predictions compared to reality, due to simplifications and assumptions. On the other hand, using only data and neglecting well-established physical laws can create the need for unreasonably large data sets that are required to exhibit low bias and are usually expensive to collect. However, fundamental but simplified physics in combination with a corrective model that compensates for possible deviations, e.g., to experimental data, can lead to physics-based predictions with low prediction errors, also despite scarce data. In this article, it is demonstrated that a hybrid model approach consisting of a physics-based model that is corrected via an artificial neural network represents an efficient prediction tool as opposed to a purely data-driven model. In particular, a semi-analytical model serves as an efficient low-fidelity model with noticeable prediction errors outside its calibration domain. An artificial neural network is used to correct the semi-analytical solution towards a desired reference solution provided by high-fidelity finite element simulations, while the efficiency of the semi-analytical model is maintained and the applicability range enhanced. We utilize residual stresses that are induced by laser shock peening as a use-case example. In addition, it is shown that non-unique relationships between model inputs and outputs lead to high prediction errors and the identification of salient input features via dimensionality analysis is highly beneficial to achieve low prediction errors. In a generalization task, predictions are also outside the process parameter space of the training region while remaining in the trained range of corrections. The corrective model predictions show substantially smaller errors than purely data-driven model predictions, which illustrates one of the benefits of the hybrid modelling approach. Ultimately, when the amount of samples in the data set is reduced, the generalization of the physics-related corrective model outperforms the purely data-driven model, which also demonstrates efficient applicability of the proposed hybrid modelling approach to problems where data is scarce.

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“…In our previous work, see [6,7] we develop a digital twins that is able to correct itself when the data stream do not provide with results in accordance with the model implemented in it. This is possible by resorting to the concept of sparse-PGD techniques, that finds a rank-1 tensor approximation to the discrepancy between the model and the observed results.…”

Section: Intr Introduction Oductionmentioning

confidence: 99%

“…If the results provided by the twin do not fit well with the built-in model, the twin is able to compute rank-1 corrections to the model via sparse-PGD methods. viding the user with useful information on hidden inf ormation on hidden information such as str ormation such as stresses or str esses or strains [6] ains [6] In general, predictions will take the form where A represents the built-in model, and B the self-learnt corrections. In the example in Fig.…”

Section: Intr Introduction Oductionmentioning

confidence: 99%

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Hybrid Twins. Part II. Real-time, data-driven modeling

González¹,

Badías²,

Chinesta³

et al. 2021

Esaform 2021

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We have seen in Part I of this paper that model order reduction allows the involvement of physics-based models in design, as in the past, but now also in online decision-making, without requiring unreasonable computing resources. On the other hand, machine learning techniques were not ready to cope with the processing speed and the lack of data. It was therefore necessary to adapt a number of techniques, and to create others, capable of operating online and even in the presence of a very small amount of data: the so-called "physics-informed artificial intelligence" techniques. For that purpose, we have adapted and proposed a number of techniques, which have proven and are proving every day in many industrial applications their capabilities and performances. Six major uses of AI in engineering concern: (i) visualization of multidimensional data; (ii) classification and clustering, supervised and unsupervised, where it is assumed that members of the same cluster have similar behaviors; (iii) model extraction, that is, discovering the quantitative relationship between inputs (actions) and outputs (reactions) in a consistent manner with respect to the physical laws. When addressing knowledge extraction, item (iv) above, as well as the need of explaining for certifying, item (v), advances are much limited and both items need for major progresses, as the one enabling discarding useless parameters, or discovering latent variables whose consideration becomes compulsory for explaining experimental findings, or combining parameters that act in a combined manner. Discovering equations is a very timely topic because it finally enables transforming data into knowledge.

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Digital twins that learn and correct themselves

Cited by 39 publications

References 44 publications

TSN: Performance Creative Choreography Based on Twin Sensor Network

TSN: Performance Creative Choreography Based on Twin Sensor Network

Hybrid Modelling by Machine Learning Corrections of Analytical Model Predictions towards High-Fidelity Simulation Solutions

Hybrid Twins. Part II. Real-time, data-driven modeling

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