A probabilistic graphical model foundation for enabling predictive digital twins at scale

Kapteyn, Michael G.; Pretorius, Jacob V. R.; Willcox, Karen

doi:10.1038/s43588-021-00069-0

Cited by 165 publications

(81 citation statements)

References 22 publications

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“…Despite the challenges of enabling this capability, future work in this direction is recommended. This work would also contribute to realizing digital twins in aircraft design [476].…”

Section: Interactive Design Optimizationmentioning

confidence: 98%

Machine Learning in Aerodynamic Shape Optimization

Li¹,

Martins²

2022

Preprint

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Large volumes of experimental and simulation aerodynamic data have been rapidly advancing aerodynamic shape optimization (ASO) via machine learning (ML), whose effectiveness has been growing thanks to continued developments in deep learning. In this review, we first introduce the state of the art and the unsolved challenges in ASO. Next, we present a description of ML fundamentals and detail the ML algorithms that have succeeded in ASO. Then we review ML applications contributing to ASO from three fundamental perspectives: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands like interactive design optimization. However, practical large-scale design optimizations remain a challenge due to the costly ML training expense. A deep coupling of ML model construction with ASO prior experience and knowledge, such as taking physics into account, is recommended to train ML models effectively.

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“…Despite the challenges of enabling this capability, future work in this direction is recommended. This work would also contribute to realizing digital twins in aircraft design [476].…”

Section: Interactive Design Optimizationmentioning

confidence: 98%

Machine Learning in Aerodynamic Shape Optimization

Li¹,

Martins²

2022

Preprint

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“…In digital twin technology there is a lack of a novel, holistic, open and universal approach to objectively perform algorithmic selection and recommend analysis approaches [32]. DSS for wind energy digital twins could include, for instance, the use of Probabilistic Graphical Model (PGMs) to represent digital twin data [33], which can prove to be a powerful abstraction for controlling and planning, as segments of PGM (between two time instances) can be seen as Partially Observable Markov Decision Processes (POMDPs). In line with this sentiment, Ref.…”

Section: Decision Support Systemsmentioning

confidence: 99%

A New Decision Process for Choosing the Wind Resource Assessment Workflow with the Best Compromise between Accuracy and Costs for a Given Project in Complex Terrain

et al. 2022

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In wind energy, the accuracy of the estimation of the wind resource has an enormous effect on the expected rate of return of a project. For a given project, the wind resource assessor is faced with a difficult choice of a wide range of simulation tools and workflows with varying accuracies (or “skill”) and costs. There is currently no guideline or process available in the industry for helping with the decision of the most “optimal” choice—and this is particularly challenging in mountainous (or “complex”) terrain. In this work, a new decision process for selecting the Wind Resource Assessment (WRA) workflow that would expect to deliver the best compromise between skill and costs for a given wind energy project is developed, with a focus on complex terrain. This involves estimating the expected skill and cost scores using a set of pre-defined weighted parameters. The new process is designed and tested by applying seven different WRA workflows to five different complex terrain sites. The quality of the decision process is then assessed for all the sites by comparing the decision made (i.e., choice of optimal workflow) using the expected skill and cost scores with the decision made using the actual skill and cost scores (obtained by comparing measurements and simulations at a validation location). The results show that the decision process works well, but the accuracy decreases as the site complexity increases. It is therefore concluded that some of the parameter weightings should be dependent on site complexity. On-going work involves collecting more data from a large range of sites, implementing measures to reduce the subjectivity of the process and developing a reliable and robust automated decision tool for the industry.

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“…[321] try to shape the actual state and a possible future of the Product Data Technologies from a Closed-Loop Product Lifecycle Management (C-L PLM) perspective, where they see an intelligent product as a product system which contains sensing, memory, data processing, reasoning and communication capabilities at four intelligence levels. [322] view the physical asset and its digital twin as two coupled dynamical systems that evolve over time through their respective state spaces, where the digital twin acquires and assimilates observational data from the asset (e.g., data from sensors or manual inspections) and uses this information to continually update its internal models so that they reflect the evolving physical system. These up-to-date internal models can then be used for analysis, prediction, optimisation and control of the physical system.…”

Section: A What Have We Done So Far?mentioning

confidence: 99%

“…However, most studies ignore this process and just predetermine the observable variables, while the fundamental question of how to identify the minimum number of observable variables has been understudied over the years and needs a systematic research and answer. As is detailed by [322], a well-designed digital twin should be comprised of models that provide a sufficiently complex digital state space, capturing variation in the physical asset that is relevant for diagnosis, prediction, and decision-making in the application of interest. On the other hand, the digital state space should be simple enough to enable tractable estimation of the digital state, even when only partially observable.…”

Section: A What Have We Done So Far?mentioning

confidence: 99%

“…For example, [332] propose a disaster city DT for enhancing disaster response and emergency management processes, where disasters that have never happened before are simulated and real world systems are extensively forecast to enable increased visibility into network dynamics of complex disaster management and humanitarian actions. The digital twining of complex networked systems are also featured with a decision-making feedback loop with dynamically updated asset specific computational models infused [322]. Especially in cases of solving multiobjective optimisation problems for complex systems using DT, which are common in analyses of entire product lifecycle in manufacturing, researchers have proposed DT frameworks aimed at multi-objective optimisation with effective feedback from different dynamics.…”

Section: A What Have We Done So Far?mentioning

confidence: 99%

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Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey

Wen¹,

Gabrys²,

Musiał³

2022

Preprint

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This paper aims to provide a comprehensive critical overview on how entities and their interactions in Complex Networked Systems (CNS) are modelled across disciplines as they approach their ultimate goal of creating a Digital Twin (DT) that perfectly matches the reality. We propose a new framework to conceptually compare diverse existing modelling paradigms from different perspectives and create unified assessment criteria to assess their respective capabilities of reaching such an ultimate goal. Using the proposed criteria, we also appraise how far the reviewed current state-of-the-art approaches are from the idealised DTs. We also identify and propose potential directions and ways of building a DT-orientated CNS based on the convergence and integration of CNS and DT utilising a variety of cross-disciplinary techniques.

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A probabilistic graphical model foundation for enabling predictive digital twins at scale

Cited by 165 publications

References 22 publications

Machine Learning in Aerodynamic Shape Optimization

Machine Learning in Aerodynamic Shape Optimization

A New Decision Process for Choosing the Wind Resource Assessment Workflow with the Best Compromise between Accuracy and Costs for a Given Project in Complex Terrain

Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey

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