rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Uncertainty Modeling for Engineering Applications
  name:
    name part: Canavero, Flavio.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: VIII, 184 p. 97 illus., 88 illus. in color. online resource.
  abstract: This book provides an overview of state-of-the-art uncertainty quantification (UQ) methodologies and applications, and covers a wide range of current research, future challenges and applications in various domains, such as aerospace and mechanical applications, structure health and seismic hazard, electromagnetic energy (its impact on systems and humans) and global environmental state change. Written by leading international experts from different fields, the book demonstrates the unifying property of UQ theme that can be profitably adopted to solve problems of different domains. The collection in one place of different methodologies for different applications has the great value of stimulating the cross-fertilization and alleviate the language barrier among areas sharing a common background of mathematical modeling for problem solution. The book is designed for researchers, professionals and graduate students interested in quantitatively assessing the effects of uncertainties in their fields of application. The contents build upon the workshop "Uncertainty Modeling for Engineering Applications" (UMEMA 2017), held in Torino, Italy in November 2017.
  table of contents: Quadrature Strategies for Constructing Polynomial Approximations -- Weighted reduced order methods for parametrized partial differential equations with random inputs -- A new approach for state estimation -- Data-efficient Sensitivity Analysis with Surrogate Modeling -- Application of Polynomial Chaos Expansions for Uncertainty Estimation in Angle-of-Arrival based Localization -- Surrogate Modeling for Fast Experimental Assessment of Specific Absorption Rate -- Stochastic Dosimetry for Radio-Frequency exposure assessment in realistic scenarios -- On the Various Applications of Stochastic Collocation in Computational Electromagnetics -- Reducing the statistical complexity of EMC testing: improvements for radiated experiments using stochastic collocation and bootstrap methods -- Hybrid Possibilistic-Probabilistic Approach to Uncertainty Quantification in Electromagnetic Compatibility Models -- Measurement uncertainty cannot always be calculated.
  note: edited by Flavio Canavero.
  subject:
    topic: Computational intelligence
  subject:
    topic: Quality control
  subject:
    topic: Reliability
  subject:
    topic: Industrial safety
  subject:
    topic: Probabilities
  subject:
    topic: Computational Intelligence
  subject:
    topic: Quality Control, Reliability, Safety and Risk
  subject:
    topic: Probability Theory and Stochastic Processes
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: PoliTO Springer Series
  identifier: 9783030048709
  identifier: https://doi.org/10.1007/978-3-030-04870-9
  record info:
    record creation date: 181229
    record change date: 20191220130322.0
    record identifier: 978-3-030-04870-9