Hermann et al., 2016 - Google Patents
Bayesian prediction of crack growth based on a hierarchical diffusion modelHermann et al., 2016
View PDF- Document ID
- 15299060083301451574
- Author
- Hermann S
- Ickstadt K
- Müller C
- Publication year
- Publication venue
- Applied Stochastic Models in Business and Industry
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Snippet
A general Bayesian approach for stochastic versions of deterministic growth models is presented to provide predictions for crack propagation in an early stage of the growth process. To improve the prediction, the information of other crack growth processes is used …
- 238000009792 diffusion process 0 title abstract description 43
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- G06F—ELECTRICAL DIGITAL DATA PROCESSING
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- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
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- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
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- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
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