Name
Abstract | ||
|---|---|---|
Complex simulations are increasingly important in systems analysis and design. In some cases simulations can be exhaustively validated against experiment and taken to be implicitly accurate. However, in domains where only limited validation of the simulations can be performed, implications of simulation studies have historically been qualitative. Validation is notably difficult in cases where experiments are expensive or otherwise prohibitive, where experimental effects are difficult to measure, and where models are thought to have unaccounted systematic error. This paper describes an approach to integrate simulation experiments with empirical data that has been applied successfully in a number of domains. This methodology generates coherent estimates of confidence in model predictions, model parameters, and estimates, i.e. calibrations, for unobserved variables. Extensions are described to integrate the results of separate experiments into a single estimate for simulation parameters, which demonstrates a new approach to model-based data fusion. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11751595_121 | ICCSA (3) |
Keywords | Field | DocType |
simulation study,multi-domain complex simulation study,simulation experiment,simulation parameter,complex simulation,model parameter,model-based data fusion,new approach,empirical data,cases simulation,model prediction,data fusion,system analysis and design,systematic error,prediction model | Data modeling,Structured systems analysis and design method,Thermal activation energy,Mathematical optimization,Systematic error,Computer science,Simulation,Algorithm,Sensor fusion,Multi domain,Calibration | Conference |
Volume | ISSN | ISBN |
3982 | 0302-9743 | 3-540-34075-0 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| James R. Gattiker | 1 | 46 | 5.83 |
| Earl Lawrence | 2 | 0 | 1.69 |
| David Higdon | 3 | 61 | 14.71 |