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Stochastic Reduced Order Models with Python (SROMPy).
| 题名： | Stochastic Reduced Order Models with Python (SROMPy). |
| 作者： | Warner, J. E. |
| 关键词： | Applications programs (computers), Computer programs, Nonintrusive measurement, Programming languages, Python (programming language), Stochastic processes, Mathematical models, Monte carlo method, Probability distribution functions, Random variables |
| 摘要： | Stochastic Reduced Order Models with Python (SROMPy) is a software package developed to enable user-friendly utilization of the stochastic reduced order model (SROM) approach for uncertainty quantification. A SROM is a low dimensional, discrete approximation to a random quantity that enables efficient and non-intrusive stochastic computations. With SROMPy, a user can easily generate a SROM to approximate a random variable or vector described by several different types of probability distributions using the Python programming language. Once a SROM is constructed, the software can be used to propagate uncertainty through a user-defined computational model to estimate statistics of a given quantity of interest. This report is meant to introduce the SROMPy module and brie y demonstrate its capabilities. A simple example of a spring-mass system with a random input is included to illustrate the practicality of the SROM approach to uncertainty quantification and relative ease of applying it with SROMPy. The example includes a comparison with a solution obtained using classical Monte Carlo simulation, demonstrating the similarities and advantages of using the SROM approach. |
| 报告类型： | 科技报告 |
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