Uncertainty quantification and model calibration

Uncertainty quantification may appear daunting for practitioners due to its inherent complexity but can be intriguing and rewarding for anyone with mathematical ambitions and genuine concern for modeling quality. Uncertainty quantification is what remains to be done when too much credibility has bee...

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Detalles Bibliográficos
Otros Autores: Jan Peter Hessling (auth), Hessling, Jan Peter, editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: [Place of publication not identified] : IntechOpen 2017
[2017]
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009438012806719
Descripción
Sumario:Uncertainty quantification may appear daunting for practitioners due to its inherent complexity but can be intriguing and rewarding for anyone with mathematical ambitions and genuine concern for modeling quality. Uncertainty quantification is what remains to be done when too much credibility has been invested in deterministic analyses and unwarranted assumptions. Model calibration describes the inverse operation targeting optimal prediction and refers to inference of best uncertain model estimates from experimental calibration data. The limited applicability of most state-of-the-art approaches to many of the large and complex calculations made today makes uncertainty quantification and model calibration major topics open for debate, with rapidly growing interest from both science and technology, addressing subtle questions such as credible predictions of climate heating.
Descripción Física:1 online resource (226 pages)
ISBN:9789535147541
9789535132806