Quantification Uncertainty Improving Efficiency (31 results)

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores four guiding themes - reduced order modelling, high dimensional problems, efficient algorithms, and applications - by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book's content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores four guiding themes - reduced order modelling, high dimensional problems, efficient algorithms, and applications - by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book's content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors.

Condition: New. 2021. Paperback. . . . . .

Condition: New. 2020. Hardcover. . . . . .

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Taschenbuch. Condition: Neu. Neuware -This book explores four guiding themes ¿ reduced order modelling, high dimensional problems, efficient algorithms, and applications ¿ by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book¿s content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 296 pp. Englisch.

Buch. Condition: Neu. Neuware -This book explores four guiding themes ¿ reduced order modelling, high dimensional problems, efficient algorithms, and applications ¿ by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book¿s content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 296 pp. Englisch.

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Condition: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.

Paperback. Condition: new. Paperback. This book explores four guiding themes reduced order modelling, high dimensional problems, efficient algorithms, and applications by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the books content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors. This book explores four guiding themes reduced order modelling, high dimensional problems, efficient algorithms, and applications by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condition: new. Hardcover. This book explores four guiding themes reduced order modelling, high dimensional problems, efficient algorithms, and applications by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the books content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors. This book explores four guiding themes reduced order modelling, high dimensional problems, efficient algorithms, and applications by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condition: Brand New. 293 pages. 9.25x6.10x0.70 inches. In Stock.

Hardcover. Condition: Brand New. 293 pages. 9.25x6.10x0.87 inches. In Stock.

Condition: New. 2021. Paperback. . . . . . Books ship from the US and Ireland.

Condition: New. 2020. Hardcover. . . . . . Books ship from the US and Ireland.

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Paperback. Condition: new. Paperback. This book explores four guiding themes reduced order modelling, high dimensional problems, efficient algorithms, and applications by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the books content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors. This book explores four guiding themes reduced order modelling, high dimensional problems, efficient algorithms, and applications by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Condition: new. Hardcover. This book explores four guiding themes reduced order modelling, high dimensional problems, efficient algorithms, and applications by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the books content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors. This book explores four guiding themes reduced order modelling, high dimensional problems, efficient algorithms, and applications by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers 4 crucial modern topics and their synergistic interaction: model order reduction, efficient solvers, high dimensional approximation and applicationsIncludes contributions, upon invitation, from world expe.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book explores four guiding themes - reduced order modelling, high dimensional problems, efficient algorithms, and applications - by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty qu.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explores four guiding themes - reduced order modelling, high dimensional problems, efficient algorithms, and applications - by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book's content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors. 296 pp. Englisch.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explores four guiding themes - reduced order modelling, high dimensional problems, efficient algorithms, and applications - by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book's content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors. 296 pp. Englisch.

Condition: New. Print on Demand.