Intelligent Engineering Component Catalogs

Bradley, Susanne; Agogino, Alice M.; Wood, William H.

doi:10.1007/978-94-011-0928-4_37

Cited by 12 publications

(5 citation statements)

References 5 publications

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“…In this context, the value of this added information is referred to as the improvement in a designer's decision-making ability. The primary metric used in engineering design is the expected value of information which accounts only for irreducible uncertainty 70,71 . To address both reducible and irreducible uncertainty, Panchal and coauthors 69 extended the expected value of information to a metric called the improvement potential (P i ).…”

Section: Value Of Informationmentioning

confidence: 99%

“…The additional information should specifically mitigate MPU while meeting performance requirements. To this end, the improvement potential (P i ) 69 metric developed by Panchal and co-authors capture the expected value of information in terms of utility 70,71 . A high improvement potential value indicates a greater value associated with refining the simulation model compared to a low value.…”

Section: Researchmentioning

confidence: 99%

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Uncertainty Management in the Design of Multiscale Systems

Sinha

Bera

Allen

et al. 2012

Journal of Mechanical Design

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In this paper, the opportunities for managing uncertainty in simulation-based design of multiscale systems are explored using constructs from information management and robust design. A comprehensive multiscale design problem, the concurrent design of material and product is used to demonstrate our approach. The desired accuracy of the simulated performance is determined by the trade-off between computational cost for model refinement and the benefits of mitigated uncertainty from the refined models. Our approach consists of integrating: (i) a robust design method for multiscale systems and (ii) an improvement potential based approach for quantifying the cost-benefit trade-off for reducing uncertainty in simulation models. Specifically, our approach focuses on allocating resources for reducing model parameter uncertainty arising due to insufficient data from simulation models. Using this approach, system level designers can efficiently allocate resources for sequential simulation model refinement in multiscale systems.

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Section: Value Of Informationmentioning

confidence: 99%

Section: Researchmentioning

confidence: 99%

Uncertainty Management in the Design of Multiscale Systems

Sinha

Bera

Allen

et al. 2012

Journal of Mechanical Design

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“…In the context of simulation-based design of multiscale systems, a simulation model is a source of information with some degree of uncertainty 15 . The improvement potential (P i ) 15 metric captures the expected value of increasing the accuracy of the information in terms of the utility of performing the calculations 35,36 . A high value of improvement potential indicates a greater value associated with refining the simulation model compared low values of improvement potential.…”

Section: Information Economicsmentioning

confidence: 99%

Managing Uncertainty in Multiscale Systems via Simulation Model Refinement

Sinha

Panchal

Allen

et al. 2011

Volume 5: 37th Design Automation Conference, Parts a and B

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The motivating question for this article is: ‘How should a system level designer allocate resources for auxiliary simulation model refinement while satisfying system level design objectives and ensuring robust process requirements in multiscale systems? Our approach consists of integrating: (i) a robust design method for multiscale systems (ii) an information economics based approach for quantifying the cost-benefit trade-off for mitigating uncertainty in simulation models. Specifically, the focus is on allocating resources for reducing model parameter uncertainty arising due to insufficient data from simulation models. A comprehensive multiscale design problem, the concurrent design of material and product is used for validation. The multiscale system is simulated with models at multiple length and time scales. The accuracy of the simulated performance is determined by the trade-off between computational cost for model refinement and the benefits of mitigated uncertainty from the refined models. System level designers can efficiently allocate resources for sequential simulation model refinement in multiscale systems using this approach.

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“…It is satisfactory for large parts of inorganic chemistry (including the Plinius domain). It is also satisfactory in the context of intelligent component catalogs [24], [25]. A component manufacturer may want to include partwhole information in the catalog.…”

Section: Lists Of Constituentsmentioning

confidence: 99%

Bottom-up construction of ontologies

Vet

Mars

1998

IEEE Trans. Knowl. Data Eng.

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Abstract-We present a particular way of building ontologies that proceeds in a bottom-up fashion. Concepts are defined in a way that mirrors the way their instances are composed out of smaller objects. The smaller objects themselves may also be modeled as being composed. Bottom-up ontologies are flexible through the use of implicit and, hence, parsimonious part-whole and subconceptsuperconcept relations. The bottom-up method complements current practice, where, as a rule, ontologies are built top-down. The design method is illustrated by an example involving ontologies of pure substances at several levels of detail. It is not claimed that bottom-up construction is a generally valid recipe; indeed, such recipes are deemed uninformative or impossible. Rather, the approach is intended to enrich the ontology developer's toolkit.Index Terms-Knowledge engineering, knowledge base management, ontology, knowledge integration, domain model, hierarchical reasoning.

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Intelligent Engineering Component Catalogs

Cited by 12 publications

References 5 publications

Uncertainty Management in the Design of Multiscale Systems

Uncertainty Management in the Design of Multiscale Systems

Managing Uncertainty in Multiscale Systems via Simulation Model Refinement

Bottom-up construction of ontologies

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