Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Квантифікація невизначеності× | Метод Монте-Карло× | |
|---|---|---|
| Галузь≠ | Імітаційне моделювання | Прийняття рішень |
| Родина≠ | Process / pipeline | MCDM |
| Рік появи≠ | Seminal modern form: 2002 | 1949 |
| Автор методу≠ | Norbert Wiener (polynomial chaos, 1938); extended to Wiener–Askey scheme by Xiu & Karniadakis (2002) | Metropolis, N., Ulam, S. |
| Тип≠ | Computational uncertainty analysis framework | Robustness wrapper — Monte Carlo uncertainty propagation |
| Основоположне джерело≠ | Xiu, D. & Karniadakis, G.E. (2002). The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations. SIAM Journal on Scientific Computing, 24(2), 619–644. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Інші назви≠ | UQ, polynomial chaos expansion, PCE, Kriging surrogate | — |
| Пов'язані≠ | 9 | 0 |
| Підсумок≠ | Uncertainty Quantification (UQ) is a computational framework for systematically measuring how uncertainty in the inputs of a model propagates into uncertainty in its outputs. Building on Wiener's polynomial chaos theory (1938) and formalised for general stochastic problems by Xiu and Karniadakis (2002), UQ uses two primary strategies: Polynomial Chaos Expansion (PCE), which represents the model output as a series of orthogonal polynomials matched to the input distributions, and Kriging (Gaussian process) surrogates, which replace an expensive simulation with a fast statistical approximation fitted to a small set of carefully chosen runs. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
| ScholarGateНабір даних ↗ |
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