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Relevant bibliographies by topics / SAPPhIRE Model of Causality

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Academic literature on the topic 'SAPPhIRE Model of Causality'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "SAPPhIRE Model of Causality"

1

Bhatt, Apoorv Naresh, Anubhab Majumder, and Amaresh Chakrabarti. "Analyzing the modes of reasoning in design using the SAPPhIRE model of causality and the Extended Integrated Model of Designing." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 35, no. 4 (2021): 384–403. http://dx.doi.org/10.1017/s0890060421000214.

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AbstractLiterature suggests that people typically understand knowledge by induction and produce knowledge by synthesis. This paper revisits the various modes of reasoning – explanatory abduction, innovative abduction, deduction, and induction – that have been proposed by earlier researchers as crucial modes of reasoning underlying the design process. First, our paper expands earlier work on abductive reasoning – an essential mode of reasoning involved in the process of synthesis – by understanding its role with the help of the “SAPPhIRE” model of causality. The explanations of abductive reason

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Sartori, Julian, Ujjwal Pal, and Amaresh Chakrabarti. "A methodology for supporting “transfer” in biomimetic design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24, no. 4 (2010): 483–506. http://dx.doi.org/10.1017/s0890060410000351.

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AbstractBiomimetics involves transfer from one or more biological examples to a technical system. This study addresses four questions. What are the essential steps in a biomimetic process? What is transferred? How can the transferred knowledge be structured in a way useful for biologists and engineers? Which guidelines can be given to support transfer in biomimetic design processes? In order to identify the essential steps involved in carrying out biomimetics, several procedures found in the literature were summarized, and four essential steps that are common across these procedures were ident

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3

Bhattacharya, Kausik, and Amaresh Chakrabarti. "A KNOWLEDGE GRAPH AND RULE BASED REASONING METHOD FOR EXTRACTING SAPPHIRE INFORMATION FROM TEXT." Proceedings of the Design Society 3 (June 19, 2023): 221–30. http://dx.doi.org/10.1017/pds.2023.23.

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AbstractRepresentation of design information using causal ontologies is very effective for creative ideation in product design. Hence researchers created databases with models of engineering and biological systems using causal ontologies. Manually building many models using technical documents requires significant effort by specialists. Researchers worked on the automatic extraction of design information leveraging the computational techniques of Machine Learning. But these methods are data intensive, have manual touch points and have not yet reported the end-to-end performance of the process.

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4

Srinivasan, V., and Amaresh Chakrabarti. "Investigating novelty–outcome relationships in engineering design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24, no. 2 (2010): 161–78. http://dx.doi.org/10.1017/s089006041000003x.

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AbstractDesign creativity involves developing novel and useful solutions to design problems. The research in this article is an attempt to understand how novelty of a design resulting from a design process is related to the kind of outcomes, described here as constructs, involved in the design process. A model of causality, the SAPPhIRE model, is used as the basis of the analysis. The analysis is based on previous research that shows that designing involves development and exploration of the seven basic constructs of the SAPPhIRE model that constitute the causal connection between the various

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5

Chakrabarti, Amaresh, V. Srinivasan, B. S. C. Ranjan, and Udo Lindemann. "A case for multiple views of function in design based on a common definition." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27, no. 3 (2013): 271–79. http://dx.doi.org/10.1017/s0890060413000279.

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AbstractFunctions are important in designing. However, several issues hinder progress with the understanding and usage of functions: lack of a clear and overarching definition of function, lack of overall justifications for the inevitability of the multiple views of function, and scarcity of systematic attempts to relate these views with one another. To help resolve these, the objectives of this research are to propose a common definition of function that underlies the multiple views in literature and to identify and validate the views of function that are logically justified to be present in

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6

Finkbeiner, Bernd, and Andrey Kupriyanov. "Causality-based Model Checking." Electronic Proceedings in Theoretical Computer Science 259 (October 10, 2017): 31–38. http://dx.doi.org/10.4204/eptcs.259.3.

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7

Iwasaki, Yumi, and Herbert A. Simon. "Causality and model abstraction." Artificial Intelligence 67, no. 1 (1994): 143–94. http://dx.doi.org/10.1016/0004-3702(94)90014-0.

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8

Gibbons, G. W. "Causality and the Skyrme model." Physics Letters B 566, no. 1-2 (2003): 171–74. http://dx.doi.org/10.1016/s0370-2693(03)00384-8.

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9

Lee, Hyun Seop, and Taek Yung Lee. "Material Removal Model of Lap Grinding for Sapphire Substrate Based on Roughness Parameters." Materials Science Forum 890 (March 2017): 384–87. http://dx.doi.org/10.4028/www.scientific.net/msf.890.384.

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Sapphire is one of difficult-to-machine materials because of its high hardness and brittleness. It can be used for an optical window or cover named as sapphire glass and a substrate for semiconductor circuits. Before preparing the required surface roughness of sapphire substrate, the geometrical shape should be retained through mechanical machining processes. The lapping and diamond mechanical polishing (DMP) are essentially used for achieving the required thickness and surface roughness of sapphire substrate prior to chemical mechanical polishing (CMP). In this study, we introduce a lap grind

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10

Martinussen, Torben. "Causality and the Cox Regression Model." Annual Review of Statistics and Its Application 9, no. 1 (2022): 249–59. http://dx.doi.org/10.1146/annurev-statistics-040320-114441.

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This article surveys results concerning the interpretation of the Cox hazard ratio in connection to causality in a randomized study with a time-to-event response. The Cox model is assumed to be correctly specified, and we investigate whether the typical end product of such an analysis, the estimated hazard ratio, has a causal interpretation as a hazard ratio. It has been pointed out that this is not possible due to selection. We provide more insight into the interpretation of hazard ratios and differences, investigating what can be learned about a treatment effect from the hazard ratio approac

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