Módszerek összehasonlítása

	Bizonytalanság-kvantifikálás	Optimalizálás szimulált helyettesítőkkel
Tudományterület	Szimuláció	Optimalizálás
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	Seminal modern form: 2002	1989 (computer experiments formulation)
Megalkotó	Norbert Wiener (polynomial chaos, 1938); extended to Wiener–Askey scheme by Xiu & Karniadakis (2002)	Sacks, Welch, Mitchell & Wynn (computer experiments framework, 1989); Kriging popularised by Matheron (1963)
Típus	Computational uncertainty analysis framework	Metamodel-assisted black-box optimization
Alapmű	Xiu, D. & Karniadakis, G.E. (2002). The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations. SIAM Journal on Scientific Computing, 24(2), 619–644.	Forrester, A., Sobester, A., & Keane, A. (2008). Engineering Design via Surrogate Modelling: A Practical Guide. Wiley.
Alternatív nevek	UQ, polynomial chaos expansion, PCE, Kriging surrogate	Vekil Model Tabanlı Optimizasyon (Surrogate-Based), metamodel-assisted optimization, surrogate modelling, emulator-based optimization
Kapcsolódó	9	5
Összefoglaló	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.	Surrogate-based optimization, formalized in the computer-experiments framework of Sacks et al. (1989) and popularized for engineering by Forrester et al. (2008), replaces a prohibitively expensive simulation or physical experiment with a cheap approximate model — called a surrogate or metamodel — and then optimizes that surrogate instead. The surrogate is typically a Kriging (Gaussian Process), Radial Basis Function, or polynomial response surface fitted to a small set of carefully chosen design evaluations and periodically updated as the search progresses.