Name
Title | ||
|---|---|---|
Monte-Carlo-Type Techniques for Processing Interval Uncertainty, and Their Potential Engineering Applications |
Abstract | ||
|---|---|---|
In engineering applications, we need to make decisions under uncertainty. Traditionally, in engineering, statistical methods are used, methods assuming that we know the probability distribution of difierent uncertain parameters. Usually, we can safely linearize the dependence of the desired quantities y (e.g., stress at difierent structural points) on the uncertain parameters xi { thus enabling sensitivity analysis. Often, the number n of uncertain parameters is huge, so sensitivity analysis leads to a lot of computation time. To speed up the processing, we propose to use special Monte-Carlo-type simulations. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/s11155-006-9021-6 | Reliable Computing |
Keywords | DocType | Volume |
interval uncertainty,engineering applications,monte-carlo techniques,monte carlo technique,monte carlo,probability distribution,sensitivity analysis | Journal | 13 |
Issue | ISSN | Citations |
1 | 1573-1340 | 5 |
PageRank | References | Authors |
0.76 | 7 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladik Kreinovich | 1 | 1091 | 281.07 |
| Jan Beck | 2 | 32 | 4.37 |
| Carlos Ferregut | 3 | 9 | 5.96 |
| Araceli Sánchez | 4 | 5 | 1.10 |
| G. Randy Keller | 5 | 5 | 0.76 |
| matthew george averill | 6 | 5 | 1.10 |
| Scott A. Starks | 7 | 61 | 12.76 |