Machine learning assisted propeller design

Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems ◽

10.1145/3450267.3452001 ◽

2021 ◽

Author(s):

Harsh Vardhan ◽

Peter Volgyesi ◽

Janos Sztipanovits

Keyword(s):

Machine Learning ◽

Propeller Design

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Learning in design: From characterizing dimensions to working systems

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060498122072 ◽

1998 ◽

Vol 12 (2) ◽

pp. 161-172 ◽

Cited By ~ 5

Author(s):

YORAM REICH

Keyword(s):

Machine Learning ◽

Decision Support ◽

Simple Model ◽

Learning Problems ◽

Marine Propeller ◽

Recent Proposal ◽

Propeller Design ◽

Learning Tasks ◽

Experienced Difficulty

The application of machine learning (ML) to solve practical problems is complex. Only recently, due to the increased promise of ML in solving real problems and the experienced difficulty of their use, has this issue started to attract attention. This difficulty arises from the complexity of learning problems and the large variety of available techniques. In order to understand this complexity and begin to overcome it, it is important to construct a characterization of learning situations. Building on previous work that dealt with the practical use of ML, a set of dimensions is developed, contrasted with another recent proposal, and illustrated with a project on the development of a decision-support system for marine propeller design. The general research opportunities that emerge from the development of the dimensions are discussed. Leading toward working systems, a simple model is presented for setting priorities in research and in selecting learning tasks within large projects. Central to the development of the concepts discussed in this paper is their use in future projects and the recording of their successes, limitations, and failures.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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