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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| Format: | eBook |
| Language: | English |
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IntechOpen
2017
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| Collection: | Directory of Open Access Books - Collection details see MPG.ReNa |
| Summary: | 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. |
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| Item Description: | Creative Commons (cc), https://creativecommons.org/licenses/by/3.0/ |
| Physical Description: | 1 electronic resource (226 p.) |
| ISBN: | 9789535132790 9789535147541 9789535132806 65579 |