Reinforcement Learning for Engineering Design Automation

Dworschak, Fabian; Dietze, Sebastian; Wittmann, Maximilian; Schleich, Benjamin; Wartzack, Sandro

doi:10.1016/j.aei.2022.101612

Cited by 29 publications

(7 citation statements)

References 41 publications

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“…RL could be used to predict designs that are manufacturable since restrictions can be implemented in the parameter grid. Furthermore, they showcased the concept of transfer learning for related design tasks in a bid to ensure automation [34].…”

Section: Contributions and Related Workmentioning

confidence: 99%

Reinforcement Learning Review: Past Acts, Present Facts and Future Prospects

Kommey,

Isaac,

Tamakloe

et al. 2024

ITJRD

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Reinforcement Learning (RL) is fast gaining traction as a major branch of machine learning, its applications have expanded well beyond its typical usage in games. Several subfields of reinforcement learning like deep reinforcement learning and multi-agent reinforcement learning are also expanding rapidly. This paper provides an extensive review on the field from the point of view of Machine Learning (ML). It begins by providing a historical perspective on the field then proceeds to lay a theoretical background on the field. It further discusses core reinforcement learning problems and approaches taken by different subfields before discussing the state of the art in the field. An inexhaustive list of applications of reinforcement learning is provided and their practicability and scalability assessed. The paper concludes by highlighting some open areas or issues in the field

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Section: Contributions and Related Workmentioning

confidence: 99%

Reinforcement Learning Review: Past Acts, Present Facts and Future Prospects

Kommey,

Isaac,

Tamakloe

et al. 2024

ITJRD

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“…Condition-based maintenance or predictive maintenance are typical scenarios where AI&ML can be leveraged (Black, Richmond, & Kolios, 2021;Carvalho et al, 2019). Engineering involves many sequential decision making; with rich information about engineering systems' status, reinforcement learning can be used to learn from simulations, experiments, routine operations, and generally experience for optimization, such as in mesh generation (Pan, Huang, Cheng, & Zeng, 2022), in manufacturing (Su, Huang, Adams, Chang, & Beling, 2022), for engineering design (Dworschak, Dietze, Wittmann, Schleich, & Wartzack, 2022). Machine learning can also be applied to even traditionally labour-intensive and time-consuming requirement elicitation process (Cheligeer et al, 2022;Mokammel et al, 2018).…”

Section: What Are the Emerging New Engineering Paradigms?mentioning

confidence: 99%

Digital engineering transformation with trustworthy AI towards industry 4.0: emerging paradigm shifts

Huang

2023

JID

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Digital engineering transformation is a crucial process for the engineering paradigm shifts in the fourth industrial revolution (4IR), and artificial intelligence (AI) is a critical enabling technology in digital engineering transformation. This article discusses the following research questions: What are the fundamental changes in the 4IR? More specifically, what are the fundamental changes in engineering? What is digital engineering? What are the main uncertainties there? What is trustworthy AI? Why is it important today? What are emerging engineering paradigm shifts in the 4IR? What is the relationship between the data-intensive paradigm and digital engineering transformation? What should we do for digitalization? From investigating the pattern of industrial revolutions, this article argues that ubiquitous machine intelligence is the defining power brought by the 4IR. Digitalization is a condition to leverage ubiquitous machine intelligence. Digital engineering transformation towards Industry 4.0 has three essential building blocks: digitalization of engineering, leveraging ubiquitous machine intelligence, and building digital trust and security. The engineering design community at large is facing an excellent opportunity to bring the new capabilities of ubiquitous machine intelligence and trustworthy AI principles, as well as digital trust, together in various engineering systems design to ensure the trustworthiness of systems in Industry 4.0.

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“…The current availability of large amounts of data has revolutionized data processing and statistical modeling techniques but, in turn, has brought new theoretical and computational challenges. Some problems have complex solutions due to scale, high dimensions, or other factors, which might require the application of multiple ML models [4] and large datasets [25]. ML has also drawn attention as a tool in resource management to dynamically manage resource scaling.…”

mentioning

confidence: 99%

“…In contrast, DL is specifically useful when working with larger, unstructured datasets, such as text and images [1]. Additional hindrances may apply in certain situations, as, for example, in some engineering design applications, heterogeneous data sources can lead to sparsity in the training data [25]. Since modern problems often require libraries that can scale for larger data sizes, a handful of ML algorithms can be parallelized through multiprocessing.…”

mentioning

confidence: 99%

Developments in Image Processing Using Deep Learning and Reinforcement Learning

Valente,

António,

Mora

et al. 2023

J. Imaging

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The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges. Alongside this evolution, data science tools have exploded in popularity over the past two decades due to their myriad of applications when dealing with complex data, their high accuracy, flexible customization, and excellent adaptability. When it comes to images, data analysis presents additional challenges because as the quality of an image increases, which is desirable, so does the volume of data to be processed. Although classic machine learning (ML) techniques are still widely used in different research fields and industries, there has been great interest from the scientific community in the development of new artificial intelligence (AI) techniques. The resurgence of neural networks has boosted remarkable advances in areas such as the understanding and processing of images. In this study, we conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges. Despite the good results that have been achieved, there are still many challenges to face in this field of study. In this work, we discuss the main and more recent improvements, applications, and developments when targeting image processing applications, and we propose future research directions in this field of constant and fast evolution.

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Reinforcement Learning for Engineering Design Automation

Cited by 29 publications

References 41 publications

Reinforcement Learning Review: Past Acts, Present Facts and Future Prospects

Reinforcement Learning Review: Past Acts, Present Facts and Future Prospects

Digital engineering transformation with trustworthy AI towards industry 4.0: emerging paradigm shifts

Developments in Image Processing Using Deep Learning and Reinforcement Learning

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